{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据格式处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "#___________________________\n",
    "def load_tmdb_movies(path):\n",
    "    df = pd.read_csv(path)\n",
    "    df['release_date'] = pd.to_datetime(df['release_date']).apply(lambda x: x.date())\n",
    "    json_columns = ['genres', 'keywords', 'production_countries',\n",
    "                    'production_companies', 'spoken_languages']\n",
    "    for column in json_columns:\n",
    "        df[column] = df[column].apply(json.loads)\n",
    "    return df\n",
    "#___________________________\n",
    "def load_tmdb_credits(path):\n",
    "    df = pd.read_csv(path)\n",
    "    json_columns = ['cast', 'crew']\n",
    "    for column in json_columns:\n",
    "        df[column] = df[column].apply(json.loads)\n",
    "    return df\n",
    "#___________________\n",
    "LOST_COLUMNS = [\n",
    "    'actor_1_facebook_likes',\n",
    "    'actor_2_facebook_likes',\n",
    "    'actor_3_facebook_likes',\n",
    "    'aspect_ratio',\n",
    "    'cast_total_facebook_likes',\n",
    "    'color',\n",
    "    'content_rating',\n",
    "    'director_facebook_likes',\n",
    "    'facenumber_in_poster',\n",
    "    'movie_facebook_likes',\n",
    "    'movie_imdb_link',\n",
    "    'num_critic_for_reviews',\n",
    "    'num_user_for_reviews']\n",
    "#____________________________________\n",
    "TMDB_TO_IMDB_SIMPLE_EQUIVALENCIES = {\n",
    "    'budget': 'budget',\n",
    "    'genres': 'genres',\n",
    "    'revenue': 'gross',\n",
    "    'title': 'movie_title',\n",
    "    'runtime': 'duration',\n",
    "    'original_language': 'language',\n",
    "    'keywords': 'plot_keywords',\n",
    "    'vote_count': 'num_voted_users'}\n",
    "#_____________________________________________________\n",
    "IMDB_COLUMNS_TO_REMAP = {'imdb_score': 'vote_average'}\n",
    "#_____________________________________________________\n",
    "def safe_access(container, index_values):\n",
    "    # return missing value rather than an error upon indexing/key failure\n",
    "    result = container\n",
    "    try:\n",
    "        for idx in index_values:\n",
    "            result = result[idx]\n",
    "        return result\n",
    "    except IndexError or KeyError:\n",
    "        return pd.np.nan\n",
    "#_____________________________________________________\n",
    "def get_director(crew_data):\n",
    "    directors = [x['name'] for x in crew_data if x['job'] == 'Director']\n",
    "    return safe_access(directors, [0])\n",
    "#_____________________________________________________\n",
    "def pipe_flatten_names(keywords):\n",
    "    return '|'.join([x['name'] for x in keywords])\n",
    "#_____________________________________________________\n",
    "def convert_to_original_format(movies, credits):\n",
    "    tmdb_movies = movies.copy()\n",
    "    tmdb_movies.rename(columns=TMDB_TO_IMDB_SIMPLE_EQUIVALENCIES, inplace=True)\n",
    "    tmdb_movies['title_year'] = pd.to_datetime(tmdb_movies['release_date']).apply(lambda x: x.year)\n",
    "    # I'm assuming that the first production country is equivalent, but have not been able to validate this\n",
    "    tmdb_movies['country'] = tmdb_movies['production_countries'].apply(lambda x: safe_access(x, [0, 'name']))\n",
    "    tmdb_movies['language'] = tmdb_movies['spoken_languages'].apply(lambda x: safe_access(x, [0, 'name']))\n",
    "    tmdb_movies['director_name'] = credits['crew'].apply(get_director)\n",
    "    tmdb_movies['actor_1_name'] = credits['cast'].apply(lambda x: safe_access(x, [1, 'name']))\n",
    "    tmdb_movies['actor_2_name'] = credits['cast'].apply(lambda x: safe_access(x, [2, 'name']))\n",
    "    tmdb_movies['actor_3_name'] = credits['cast'].apply(lambda x: safe_access(x, [3, 'name']))\n",
    "    tmdb_movies['genres'] = tmdb_movies['genres'].apply(pipe_flatten_names)\n",
    "    tmdb_movies['plot_keywords'] = tmdb_movies['plot_keywords'].apply(pipe_flatten_names)\n",
    "    return tmdb_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[{'cast_id': 242, 'character': 'Jake Sully', '...</td>\n",
       "      <td>[{'credit_id': '52fe48009251416c750aca23', 'de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>[{'cast_id': 4, 'character': 'Captain Jack Spa...</td>\n",
       "      <td>[{'credit_id': '52fe4232c3a36847f800b579', 'de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'James Bond', 'cr...</td>\n",
       "      <td>[{'credit_id': '54805967c3a36829b5002c41', 'de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[{'cast_id': 2, 'character': 'Bruce Wayne / Ba...</td>\n",
       "      <td>[{'credit_id': '52fe4781c3a36847f81398c3', 'de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>[{'cast_id': 5, 'character': 'John Carter', 'c...</td>\n",
       "      <td>[{'credit_id': '52fe479ac3a36847f813eaa3', 'de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{'cast_id': 242, 'character': 'Jake Sully', '...   \n",
       "1  [{'cast_id': 4, 'character': 'Captain Jack Spa...   \n",
       "2  [{'cast_id': 1, 'character': 'James Bond', 'cr...   \n",
       "3  [{'cast_id': 2, 'character': 'Bruce Wayne / Ba...   \n",
       "4  [{'cast_id': 5, 'character': 'John Carter', 'c...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{'credit_id': '52fe48009251416c750aca23', 'de...  \n",
       "1  [{'credit_id': '52fe4232c3a36847f800b579', 'de...  \n",
       "2  [{'credit_id': '54805967c3a36829b5002c41', 'de...  \n",
       "3  [{'credit_id': '52fe4781c3a36847f81398c3', 'de...  \n",
       "4  [{'credit_id': '52fe479ac3a36847f813eaa3', 'de...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math, nltk, warnings\n",
    "from nltk.corpus import wordnet\n",
    "from sklearn import linear_model\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from fuzzywuzzy import fuzz\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "plt.rcParams[\"patch.force_edgecolor\"] = True\n",
    "plt.style.use('fivethirtyeight')\n",
    "mpl.rc('patch', edgecolor = 'dimgray', linewidth=1)\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"last_expr\"\n",
    "pd.options.display.max_columns = 50\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')\n",
    "PS = nltk.stem.PorterStemmer()\n",
    "#__________________\n",
    "# load the dataset\n",
    "credits = load_tmdb_credits(\"tmdb_5000_credits.csv\")\n",
    "credits.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>237000000</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{'id': 1463, 'name': 'culture clash'}, {'id':...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{'name': 'Ingenious Film Partners', 'id': 289...</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>2009-12-10</td>\n",
       "      <td>2787965087</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
       "      <td>285</td>\n",
       "      <td>[{'id': 270, 'name': 'ocean'}, {'id': 726, 'na...</td>\n",
       "      <td>en</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>[{'name': 'Walt Disney Pictures', 'id': 2}, {'...</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>2007-05-19</td>\n",
       "      <td>961000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>At the end of the world, the adventure begins.</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>245000000</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...</td>\n",
       "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
       "      <td>206647</td>\n",
       "      <td>[{'id': 470, 'name': 'spy'}, {'id': 818, 'name...</td>\n",
       "      <td>en</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>[{'name': 'Columbia Pictures', 'id': 5}, {'nam...</td>\n",
       "      <td>[{'iso_3166_1': 'GB', 'name': 'United Kingdom'...</td>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>880674609</td>\n",
       "      <td>148.0</td>\n",
       "      <td>[{'iso_639_1': 'fr', 'name': 'Français'}, {'is...</td>\n",
       "      <td>Released</td>\n",
       "      <td>A Plan No One Escapes</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250000000</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 80, 'nam...</td>\n",
       "      <td>http://www.thedarkknightrises.com/</td>\n",
       "      <td>49026</td>\n",
       "      <td>[{'id': 849, 'name': 'dc comics'}, {'id': 853,...</td>\n",
       "      <td>en</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>[{'name': 'Legendary Pictures', 'id': 923}, {'...</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>1084939099</td>\n",
       "      <td>165.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Legend Ends</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>260000000</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...</td>\n",
       "      <td>http://movies.disney.com/john-carter</td>\n",
       "      <td>49529</td>\n",
       "      <td>[{'id': 818, 'name': 'based on novel'}, {'id':...</td>\n",
       "      <td>en</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>[{'name': 'Walt Disney Pictures', 'id': 2}]</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>284139100</td>\n",
       "      <td>132.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Lost in our world, found in another.</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                                             genres  \\\n",
       "0  237000000  [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...   \n",
       "1  300000000  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  245000000  [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...   \n",
       "3  250000000  [{'id': 28, 'name': 'Action'}, {'id': 80, 'nam...   \n",
       "4  260000000  [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...   \n",
       "\n",
       "                                       homepage      id  \\\n",
       "0                   http://www.avatarmovie.com/   19995   \n",
       "1  http://disney.go.com/disneypictures/pirates/     285   \n",
       "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
       "3            http://www.thedarkknightrises.com/   49026   \n",
       "4          http://movies.disney.com/john-carter   49529   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  [{'id': 1463, 'name': 'culture clash'}, {'id':...                en   \n",
       "1  [{'id': 270, 'name': 'ocean'}, {'id': 726, 'na...                en   \n",
       "2  [{'id': 470, 'name': 'spy'}, {'id': 818, 'name...                en   \n",
       "3  [{'id': 849, 'name': 'dc comics'}, {'id': 853,...                en   \n",
       "4  [{'id': 818, 'name': 'based on novel'}, {'id':...                en   \n",
       "\n",
       "                             original_title  \\\n",
       "0                                    Avatar   \n",
       "1  Pirates of the Caribbean: At World's End   \n",
       "2                                   Spectre   \n",
       "3                     The Dark Knight Rises   \n",
       "4                               John Carter   \n",
       "\n",
       "                                            overview  popularity  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
       "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
       "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
       "3  Following the death of District Attorney Harve...  112.312950   \n",
       "4  John Carter is a war-weary, former military ca...   43.926995   \n",
       "\n",
       "                                production_companies  \\\n",
       "0  [{'name': 'Ingenious Film Partners', 'id': 289...   \n",
       "1  [{'name': 'Walt Disney Pictures', 'id': 2}, {'...   \n",
       "2  [{'name': 'Columbia Pictures', 'id': 5}, {'nam...   \n",
       "3  [{'name': 'Legendary Pictures', 'id': 923}, {'...   \n",
       "4        [{'name': 'Walt Disney Pictures', 'id': 2}]   \n",
       "\n",
       "                                production_countries release_date     revenue  \\\n",
       "0  [{'iso_3166_1': 'US', 'name': 'United States o...   2009-12-10  2787965087   \n",
       "1  [{'iso_3166_1': 'US', 'name': 'United States o...   2007-05-19   961000000   \n",
       "2  [{'iso_3166_1': 'GB', 'name': 'United Kingdom'...   2015-10-26   880674609   \n",
       "3  [{'iso_3166_1': 'US', 'name': 'United States o...   2012-07-16  1084939099   \n",
       "4  [{'iso_3166_1': 'US', 'name': 'United States o...   2012-03-07   284139100   \n",
       "\n",
       "   runtime                                   spoken_languages    status  \\\n",
       "0    162.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "1    169.0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "2    148.0  [{'iso_639_1': 'fr', 'name': 'Français'}, {'is...  Released   \n",
       "3    165.0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "4    132.0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "\n",
       "                                          tagline  \\\n",
       "0                     Enter the World of Pandora.   \n",
       "1  At the end of the world, the adventure begins.   \n",
       "2                           A Plan No One Escapes   \n",
       "3                                 The Legend Ends   \n",
       "4            Lost in our world, found in another.   \n",
       "\n",
       "                                      title  vote_average  vote_count  \n",
       "0                                    Avatar           7.2       11800  \n",
       "1  Pirates of the Caribbean: At World's End           6.9        4500  \n",
       "2                                   Spectre           6.3        4466  \n",
       "3                     The Dark Knight Rises           7.6        9106  \n",
       "4                               John Carter           6.1        2124  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = load_tmdb_movies(\"tmdb_5000_movies.csv\")\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>plot_keywords</th>\n",
       "      <th>language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>gross</th>\n",
       "      <th>duration</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>title_year</th>\n",
       "      <th>country</th>\n",
       "      <th>director_name</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>237000000</td>\n",
       "      <td>Action|Adventure|Fantasy|Science Fiction</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>culture clash|future|space war|space colony|so...</td>\n",
       "      <td>English</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{'name': 'Ingenious Film Partners', 'id': 289...</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>2009-12-10</td>\n",
       "      <td>2787965087</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Zoe Saldana</td>\n",
       "      <td>Sigourney Weaver</td>\n",
       "      <td>Stephen Lang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300000000</td>\n",
       "      <td>Adventure|Fantasy|Action</td>\n",
       "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
       "      <td>285</td>\n",
       "      <td>ocean|drug abuse|exotic island|east india trad...</td>\n",
       "      <td>English</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>[{'name': 'Walt Disney Pictures', 'id': 2}, {'...</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>2007-05-19</td>\n",
       "      <td>961000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>At the end of the world, the adventure begins.</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>Keira Knightley</td>\n",
       "      <td>Stellan Skarsgård</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>245000000</td>\n",
       "      <td>Action|Adventure|Crime</td>\n",
       "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
       "      <td>206647</td>\n",
       "      <td>spy|based on novel|secret agent|sequel|mi6|bri...</td>\n",
       "      <td>Français</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>[{'name': 'Columbia Pictures', 'id': 5}, {'nam...</td>\n",
       "      <td>[{'iso_3166_1': 'GB', 'name': 'United Kingdom'...</td>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>880674609</td>\n",
       "      <td>148.0</td>\n",
       "      <td>[{'iso_639_1': 'fr', 'name': 'Français'}, {'is...</td>\n",
       "      <td>Released</td>\n",
       "      <td>A Plan No One Escapes</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Léa Seydoux</td>\n",
       "      <td>Ralph Fiennes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250000000</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "      <td>http://www.thedarkknightrises.com/</td>\n",
       "      <td>49026</td>\n",
       "      <td>dc comics|crime fighter|terrorist|secret ident...</td>\n",
       "      <td>English</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>[{'name': 'Legendary Pictures', 'id': 923}, {'...</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>1084939099</td>\n",
       "      <td>165.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Legend Ends</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Michael Caine</td>\n",
       "      <td>Gary Oldman</td>\n",
       "      <td>Anne Hathaway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>260000000</td>\n",
       "      <td>Action|Adventure|Science Fiction</td>\n",
       "      <td>http://movies.disney.com/john-carter</td>\n",
       "      <td>49529</td>\n",
       "      <td>based on novel|mars|medallion|space travel|pri...</td>\n",
       "      <td>English</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>[{'name': 'Walt Disney Pictures', 'id': 2}]</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>284139100</td>\n",
       "      <td>132.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Lost in our world, found in another.</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Andrew Stanton</td>\n",
       "      <td>Lynn Collins</td>\n",
       "      <td>Samantha Morton</td>\n",
       "      <td>Willem Dafoe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                                    genres  \\\n",
       "0  237000000  Action|Adventure|Fantasy|Science Fiction   \n",
       "1  300000000                  Adventure|Fantasy|Action   \n",
       "2  245000000                    Action|Adventure|Crime   \n",
       "3  250000000               Action|Crime|Drama|Thriller   \n",
       "4  260000000          Action|Adventure|Science Fiction   \n",
       "\n",
       "                                       homepage      id  \\\n",
       "0                   http://www.avatarmovie.com/   19995   \n",
       "1  http://disney.go.com/disneypictures/pirates/     285   \n",
       "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
       "3            http://www.thedarkknightrises.com/   49026   \n",
       "4          http://movies.disney.com/john-carter   49529   \n",
       "\n",
       "                                       plot_keywords  language  \\\n",
       "0  culture clash|future|space war|space colony|so...   English   \n",
       "1  ocean|drug abuse|exotic island|east india trad...   English   \n",
       "2  spy|based on novel|secret agent|sequel|mi6|bri...  Français   \n",
       "3  dc comics|crime fighter|terrorist|secret ident...   English   \n",
       "4  based on novel|mars|medallion|space travel|pri...   English   \n",
       "\n",
       "                             original_title  \\\n",
       "0                                    Avatar   \n",
       "1  Pirates of the Caribbean: At World's End   \n",
       "2                                   Spectre   \n",
       "3                     The Dark Knight Rises   \n",
       "4                               John Carter   \n",
       "\n",
       "                                            overview  popularity  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
       "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
       "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
       "3  Following the death of District Attorney Harve...  112.312950   \n",
       "4  John Carter is a war-weary, former military ca...   43.926995   \n",
       "\n",
       "                                production_companies  \\\n",
       "0  [{'name': 'Ingenious Film Partners', 'id': 289...   \n",
       "1  [{'name': 'Walt Disney Pictures', 'id': 2}, {'...   \n",
       "2  [{'name': 'Columbia Pictures', 'id': 5}, {'nam...   \n",
       "3  [{'name': 'Legendary Pictures', 'id': 923}, {'...   \n",
       "4        [{'name': 'Walt Disney Pictures', 'id': 2}]   \n",
       "\n",
       "                                production_countries release_date       gross  \\\n",
       "0  [{'iso_3166_1': 'US', 'name': 'United States o...   2009-12-10  2787965087   \n",
       "1  [{'iso_3166_1': 'US', 'name': 'United States o...   2007-05-19   961000000   \n",
       "2  [{'iso_3166_1': 'GB', 'name': 'United Kingdom'...   2015-10-26   880674609   \n",
       "3  [{'iso_3166_1': 'US', 'name': 'United States o...   2012-07-16  1084939099   \n",
       "4  [{'iso_3166_1': 'US', 'name': 'United States o...   2012-03-07   284139100   \n",
       "\n",
       "   duration                                   spoken_languages    status  \\\n",
       "0     162.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "1     169.0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "2     148.0  [{'iso_639_1': 'fr', 'name': 'Français'}, {'is...  Released   \n",
       "3     165.0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "4     132.0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "\n",
       "                                          tagline  \\\n",
       "0                     Enter the World of Pandora.   \n",
       "1  At the end of the world, the adventure begins.   \n",
       "2                           A Plan No One Escapes   \n",
       "3                                 The Legend Ends   \n",
       "4            Lost in our world, found in another.   \n",
       "\n",
       "                                movie_title  vote_average  num_voted_users  \\\n",
       "0                                    Avatar           7.2            11800   \n",
       "1  Pirates of the Caribbean: At World's End           6.9             4500   \n",
       "2                                   Spectre           6.3             4466   \n",
       "3                     The Dark Knight Rises           7.6             9106   \n",
       "4                               John Carter           6.1             2124   \n",
       "\n",
       "   title_year                   country      director_name     actor_1_name  \\\n",
       "0      2009.0  United States of America      James Cameron      Zoe Saldana   \n",
       "1      2007.0  United States of America     Gore Verbinski    Orlando Bloom   \n",
       "2      2015.0            United Kingdom         Sam Mendes  Christoph Waltz   \n",
       "3      2012.0  United States of America  Christopher Nolan    Michael Caine   \n",
       "4      2012.0  United States of America     Andrew Stanton     Lynn Collins   \n",
       "\n",
       "       actor_2_name       actor_3_name  \n",
       "0  Sigourney Weaver       Stephen Lang  \n",
       "1   Keira Knightley  Stellan Skarsgård  \n",
       "2       Léa Seydoux      Ralph Fiennes  \n",
       "3       Gary Oldman      Anne Hathaway  \n",
       "4   Samantha Morton       Willem Dafoe  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_initial = convert_to_original_format(movies, credits)\n",
    "df_initial.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据情况展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>column type</th>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>844</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>30</td>\n",
       "      <td>53</td>\n",
       "      <td>63</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null values (%)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64.3556</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.79055</td>\n",
       "      <td>0</td>\n",
       "      <td>0.062461</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0208203</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0416406</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.5724</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0208203</td>\n",
       "      <td>3.62274</td>\n",
       "      <td>0.62461</td>\n",
       "      <td>1.10348</td>\n",
       "      <td>1.31168</td>\n",
       "      <td>1.93629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                budget  genres homepage     id plot_keywords language  \\\n",
       "column type      int64  object   object  int64        object   object   \n",
       "null values          0       0     3091      0             0       86   \n",
       "null values (%)      0       0  64.3556      0             0  1.79055   \n",
       "\n",
       "                original_title  overview popularity production_companies  \\\n",
       "column type             object    object    float64               object   \n",
       "null values                  0         3          0                    0   \n",
       "null values (%)              0  0.062461          0                    0   \n",
       "\n",
       "                production_countries release_date  gross   duration  \\\n",
       "column type                   object       object  int64    float64   \n",
       "null values                        0            1      0          2   \n",
       "null values (%)                    0    0.0208203      0  0.0416406   \n",
       "\n",
       "                spoken_languages  status  tagline movie_title vote_average  \\\n",
       "column type               object  object   object      object      float64   \n",
       "null values                    0       0      844           0            0   \n",
       "null values (%)                0       0  17.5724           0            0   \n",
       "\n",
       "                num_voted_users title_year  country director_name  \\\n",
       "column type               int64    float64   object        object   \n",
       "null values                   0          1      174            30   \n",
       "null values (%)               0  0.0208203  3.62274       0.62461   \n",
       "\n",
       "                actor_1_name actor_2_name actor_3_name  \n",
       "column type           object       object       object  \n",
       "null values               53           63           93  \n",
       "null values (%)      1.10348      1.31168      1.93629  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# info on variable types and filling factor\n",
    "tab_info=pd.DataFrame(df_initial.dtypes).T.rename(index={0:'column type'})\n",
    "tab_info=tab_info.append(pd.DataFrame(df_initial.isnull().sum()).T.rename(index={0:'null values'}))\n",
    "tab_info=tab_info.append(pd.DataFrame(df_initial.isnull().sum()/df_initial.shape[0]*100).T.\n",
    "                         rename(index={0:'null values (%)'}))\n",
    "tab_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关键词信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "set_keywords = set()\n",
    "for liste_keywords in df_initial['plot_keywords'].str.split('|').values:\n",
    "    if isinstance(liste_keywords, float): continue  # only happen if liste_keywords = NaN\n",
    "    set_keywords = set_keywords.union(liste_keywords)\n",
    "#_________________________\n",
    "# remove null chain entry\n",
    "set_keywords.remove('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "统计关键词频率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def count_word(df, ref_col, liste):\n",
    "    keyword_count = dict()\n",
    "    for s in liste: keyword_count[s] = 0\n",
    "    for liste_keywords in df[ref_col].str.split('|'):        \n",
    "        if type(liste_keywords) == float and pd.isnull(liste_keywords): continue        \n",
    "        for s in [s for s in liste_keywords if s in liste]: \n",
    "            if pd.notnull(s): keyword_count[s] += 1\n",
    "    #______________________________________________________________________\n",
    "    # convert the dictionary in a list to sort the keywords by frequency\n",
    "    keyword_occurences = []\n",
    "    for k,v in keyword_count.items():\n",
    "        keyword_occurences.append([k,v])\n",
    "    keyword_occurences.sort(key = lambda x:x[1], reverse = True)\n",
    "    return keyword_occurences, keyword_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['woman director', 324],\n",
       " ['independent film', 318],\n",
       " ['duringcreditsstinger', 307],\n",
       " ['based on novel', 197],\n",
       " ['murder', 189]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_occurences, dum = count_word(df_initial, 'plot_keywords', set_keywords)\n",
    "keyword_occurences[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关键词情况展示：词云和统计图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHPsJ8MlPMj/HmVQ2AL/1Hzk/AXwe//Pv8HrqGgXGx78ItO6huDgXWpzC9Af/nev/fKKz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O3jvt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\n4THgg+8Bb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DPpllRUDhx8galSC6WGAEhkVa0UHeh2Z6dkG+4Belvp7Pv66ABGwxwXVjvv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\nBW6dlR0Li4VpihX+BBi5VbJUVIW1Trbs5/iRSKqN22eAWI7U8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5+hp53VUqgURwpZVc7I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Tbt9WGp+k/ku6qeNWSop3nTSJrMUpkqsriSz/DMmhRIy7LrtO8PyN0kTjmfTPsHVyoaFppqlo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\nL8/tGmW18ZirG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s+xlsdKGPwo8Azg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SH7+D6s8ryrxsVVTv346cIq5CXvV1HkVztxPRQQ3bI1t8Aq14zOPLtSegGF7d5Kt+mMMX\n0PzuMtQ270YnLT6Gyv7ebHAafZlDHIEWON6NxoWXIPH73cDv+zEvrsa+6bT639XRItli9kB/FnuG\nGhayVkz+iISbI5AXzE3A/DJJ3BqtVjbaQlPrGFdCk/vVUAOdgDqChSvIEbFpdrcloGqAN6LGvzEa\nkNagJfJsiwywO9o+01t+LwH+pwwqG6OB9Ba0Haub06RG03LrvL/kaTrwRJmobUJ3R/1W+auf1vRu\n5KlxKzLE7i7vvRIZZD2d1lQZtS8vn7u6XFMfwG4o+d6KzsEQ/0VLaJiC4hg8hYS16hShVdBEZm00\nyBzccNCsXMlnIgP9mj6u62ZV4aNou+Oskr+pSISZjJ7hNOTO/osGRsiRyBD4XGbeFhF70BIUz0ae\nfE2DNY6gtc1iK2SEPFpeB7WTebSCgJ8AfK/Tinu0tqO8npawelVJdzO0Pa46RfOraCtnxwMXisFx\nHapv5yHj88MR8X+orn8ATaabrkjOAH4GfD0ibkcTtZlocv5CJISfVb57sVW+iNgReVWORJOyXdCW\nkcdRXI27Ub/zKWTUntx0tbB2zTVIfNgVGfOvBC6NiJ+jgOJ9irS9pL2gCCVHIM++jVG93B614cOy\nBMTuwujYqaRXCfjV1uvj0ZbKoyLivdksKPiqyAj+J2rTb0DeFtVprMNR29mhU0K1570JutdDy+t1\nj59xSAS5peS7CVX9vw+Jp2NRm16T1lhVbZ+uRK5uRMfr6OcJs218BY1Fh9E6vKESuc9HZdO1l255\nfscgT6+qvO6LiItQPJ1b+vp8SWM8imcyGZXB/T310aEtHO9GgnhfbIFWhFeldUz52khEXwdNdtZB\nY/TEzPxkg1utt4FrkcB7WEQchryTujb660mX37ui/ry/K/bAQkH2w6guronuf01kQ+1RLrsfxUVr\nwnxa3tbblXTfSGv7edUeJyCh8HPlc036t9mUo+1L3l+FxodRtLa2jytpr13e68vrrdq68tkiBs2M\niPnArUVwfBfyBm9ySEk1fu1T8vHdhv1Wb+kNT3mj7IX6lxuQuDGu3Nu6aEx8DhITLm4yHtbSH4Xs\nglehMpmBbKSHS3qzI+L0zJzaeyotUkHKD0N95c6ovA9GfflkFLurkdd3jZuAMyLizblomIFNkEDw\nDNQH98aZyNZbHfWpn0YeWlegMtoB2ROVcPB1FIOrt3ucGxEblHIO9Own0Oor1kX9xVooPlugutVp\n/K7KbDc0Th2W8sSvuDAirkOLsY3Hg+p7I2IzJLpsjPrfJ0q+d0B25o4R8caGAv3u6Fm+LxXyo/qu\nUaht7o/6vLP7svmKwLleapfFeugAqKpfXNB27SspB7o0ofadl6Fg/iPRQty3I+JeVP4fQjb1PeUz\n3Qol1ZiwUyW8pDzRZkTEycg23p82IavkbVK5r/nAfdnLoSlN7b629Osi3kmovmyOyv6ZqOxGob7x\nKiRk9jnPiYjNSzmdisTEfnkRt6VZ9ZfPRnVw25KnR0NhJs7KAT50bLBjIWsFpAzYn4yIi5FR9xyk\nrl+NYoxc0dSQiNaWg0NZ9KSr0bS2PFRxfro5JrreqZ5c8vnV0JbA9ZAb8jposP83pbPurZOIiLWQ\n8fETtKXiero77r7KU5X+9cgz4ISQZ8WWtDykdkKD/tndpl/jBDSh/m5E/BA9wzUi4i1oVeI0ej45\nrcrfeFqBKNs9smYig65jGafifFQC0z/Qs67KdmVak5gxqIyryWDTbTirohWzl5T07kGutuc3NA56\nYn+0TehfSBAchQaj/0HBwO9CHmufD22FbT9xpM62wLHZ2qq1Gi2BciYy5jrGACkD2Jm1l36KAuVX\nR4tXx41XcTw2p/kxzNW2q91QGzsQGe9VkOWqTY6jeLg1NNqrOEQTy/8noVMwLy3fOQIZuI1InYB4\nMhLXfoue26qoLm+EjKOjyrU9teN3lmsOyszrQ7EbTkGG0acy84xi5H0HeH9EXNDtgF6M/qvRFrOz\nUH3ZDQk7l4Tc3i/qUlgltSX680jIWx+V9SPAH7pNq7Tl8Ujgj4h4Fqqnz05tZ3kMeU428irJzOkR\n8Q5ap7aOpbXKWf2u+pFOhljlOVmd0nZF+Y76PU5HfdfrUJ/eUfTORb0XLkUxbT6L+pynQqcLHos8\nF65t+0yfFyDv9gAAIABJREFUlInEO9DWz7lo9fQG4KouRNpKAHouMtBvLP+PoOWl8gTwjG77tZK/\nryLR5HTUB6+ChNC90cLMW7OPrcpl8rgXinEzC/ULR4Xii1RxvR5FbfIlSOTqc/JcG0tmovIcEOO5\nJiociDy9QZPbmyPiAdQvPogEhL9n7bTJDvmt6u2dtLZGBa3Fn+xmklHy+P2SxkrlZwQaI16LxrVj\nukjvsYg4ORV78cOoTf+QVtzEVVEfPh95wFxTPtf11pHaAtJi1Gy6Xu2pksdjkTj771D8xOFoMWIj\nZK99tGF2qvHrnWgR57QqH/25N1rluSuyWQ7IXrxJa3ZRx/Gw1vedgMr2PNRmVkf21JaonDZEHje9\nClnlGVd1LzPz+xFxJao3G6E+9Ap0InCj7WYVRVT8FBqrv47sISLidWjh7SG00NlXGnMpXmBlgex+\nJAb9vJoblIn0Cag/+nyDfFVeakmH0ztLm2oiklR15wVoq9/D5fMrlc8/hWzAo5BN1avY1kO685FY\nuQGwV7Z57kXE75BAsS8SejrV143K919Sy2OUtvRFJFpX2+T78rLcHi1wVtvrtg6dGl/14Y8i77mN\nUZ9RzXO6EVC+gAKUzy120POQOP8BNEYeSQ9xE/ui1n4mIntipfp75c95qD69tpc0qq2sr0CC8Thk\nT1Rxnut1rF+kdlf06g1Y5ixjyrV9iVgjkdj1G+TkcXVmTu/t+iaUZ7gg5IH4XWRHXoYW9TZA4+VH\nQqcM/2MghLMhQQ6CiPP+WbY/aBBe7GQn+nGCVBffuaSnVLwZDQKP0Vrlnoe8CDZs8Plnls/8B03I\nd6Sc7EHZY9yPPO2ERJ7r0UD/CPIUeRAZEf0+tbCk/yrk1lptD3sSGXrf6C2/1XNGAt+twHY9XFMF\n/9zxaa6Hz0TG0lxktP2D1pbUL/b3+aHYT9VAXH99AhIX90CG59XIEB/bSzorozgMH6v9vwDYtvz/\nVrRFcZm0gU7plvs+fml+HzLS90feGs/tZxoTkWfJSUhYPg95qazR4XMXls8Mq732N7RNun4643ZI\nUH1bP/O32KmEyJPmrlL+jU9cW0rlvVqpl+8q/x+Hjo4HGYe7omDTT0feqlOPqu0cW5T/V6Z1Stdw\nJED+ucpzl9/xOiRS/7eUx+20ths2Om23ltZ6qI99ovSXt9AKFnwsXZz8g7wcHgFeXf7fCvXZ65f/\n90GHVzRNr2rX25Q+cbF6h7wG70die69tvjzzt6KJ11nluV2Gtkv/uzy/W0odr7wE+8rb6sBLqjKn\ni9M2u7j/HdG2sENKfTkHiZg3I7GxfnJWN6d9vgl5Om9NP08ya1J2pa86sa9y6ePzY5EX24DnraS/\nChIzXoM8HDdH28YXq38d0tkBiebXlPpzB/JWeH8XaVT1/H3oBNa1+7quQXpVH1TZAVX6VYy56kTE\n/p5iNxWNB4vZJ6Xcl8ju6+1++vG5N6A+8WAkgs9AwmjTU3er070vRIvJ1XOt9+XPL/3IK7rI12i0\n+LTqkvYbtTx9DdnIi506jhYppqIFhqbpVqfOHYcWHKtYRCPLT5TX/gicXP9MH2l+AI01i81XkGh9\nTZX/vsqcVuzAT6C50CWl3fwJ2dDXILv6PjQPaHRCd9t3TGj7fzhaCH4DDU5L75D2Rmj8Pqu9TSO7\n/B9IwO3ps1VbPgTZjqv2dd0S1qkq7toOlIW5LtMZhXao/ActjH27tJeu0umlXv6yPKeXtr2/BRrP\n/0o5BXVF+LFH1gpGREwAfgxsGxFTaK0IZcpj4jnIiH13h3Qmoo7zXlqxfR6t/TyGPJQeR5P9fm8J\nKKryuaGgzq9E21bmI4Pp8iZpZ+atEfEKtGXiPWhQ+VtEnJqZf6RLt+OS5t9C7vB7oQ5kLdTpHQ38\nNPtx2l5bnqsTB5+PvDimI3f9Xlfes/RmyHNhd+C0iPg+MjKnIvHoFCQAdBN3oXK13hatlG+DJhL3\no8nFn7IWUL4hp5Z72rHkbTgycF6PVnzupH/B2l+LYttMCW2Pq8praijI4i/RBPYryHunt1WVBcjw\n+2LoBLsHkXg6IxRMf19asY06UiubhUTENsitfh6tuARVPK/JPX2m7fOrosnyk2j19bCQC//9nT7b\niZL2i1DQ53+XOnAfre2L/SK1Pe8n5acbRiEhd3hEkFp92wK4pO1eH0GeC/2JkUUWj5nSF1ZbkJ+B\nBI6N6dkTskfKquuDqB+civrER9CK6X8ppxlm5uVdZHEGcvf/UUR8CBlb1QriKHSK6+3l+xt5NRRP\ntj1RjIpRqP++Fnk2Xp/NT72pyuGuks7ewGfb+ugXoUDb57Z9piOlDp4fEQ+WNDZAHo13At/Jhtt5\naiuVx6Hx5IPI2HwS9eO7omf6eESc2OQZorZ7KXBcKE7khPLao6Hg/LuifrcplXfbxuX/35e8Vyv5\n8zLzDxHxCzQuQi8B7kv5/Q74XeiQiunIS2E+EkbHl9+jUfyPHk+OqvFS5DmzHlod/79Q7JsZaPyv\n4jndj+r8NZn5r17S6pHMvCoirqW20l7uvTqZaTzFK6Bp/Syee2ciIW5z4PKIuAe1w4dRO53Uj7Gs\nPe8ZipNZbccdTsMDdErdnI7ib/0PmtxVB8k8gkTX6d3277UxfH20cv8uNFkbRSu2zkwkch2bmUf2\n1n8UL0Ey8+qIOABN9FYv+bwmW3Ewu8ljIlHtMxHxs3Kv01CcsaeaplXL7+FoIvlMdBrpkmxLBRb2\nkzNR3MSZledPyXvq6/u2+4on0x/RLoLJtALuV3Ww+plc7rtJ/10FeZ5OOVE5dSDN+khQfQCdFNx0\nKze0vOpHo0lx5e1Sf46TUTtauUEeK8/b3dD4dS/aWVFtzbwPhQd4BAmi12XneKtVHo9HO0tODsVy\nvRrVxa2QZ+AlqH9vSlXXHqOE4Uid2ljfprk+sgsuaZhmFcj/hxFxBhLk5yNb5SAk7lShQHot89Rh\nKZXX4pbIjr6LRU++HoP6iMbbCmv3tQU6/Xb71OmKw0q5XV7eXz8iXpsNYkT1kv97IuLrqA7cFhEX\noHq0AfJKmwr8P3vnHWZXVf39z0ojvZIASQggvUh/KYIiRWkiIIIF+NG7CCIC0kFAunQpiiAoIIL0\nXqUKSK+GEloI6T2TSTLr/eO7ds6Zm1vOmYkoJut57jPlnrvvPmfvvfr6rkZl6MNRMPQnZnZLfGYa\nal7W0h7d15XxtDkK2K6IePesmOsl7v5IkUwnV+bi4aFH7owC6LsDD5nZle5+X1umFz83Qjz6SZgL\nA+LuPtzMDkcVD30pXtXxpSZrx3ovpC8hhSL3DmpZXq2l9g7A9e7es8E4SyKFcCZixN2QQtQZHXwj\n6yRhSCj96N+R6hiOpbkOuYLX/xDVem+CDMrfo/tuS0et+UqmdPdeiGlNr6WAmdLTa95zMOOjkaE7\nEzG2zgjraFcv0GkuxkkK8E4oMjcRORGbUXRlLZRJdaC7v1V7pFZjps4833X3h6q8fymK8q1WZLzc\n57qh/X23ux9U5f1DgLPcvacJIPt2d+9WZ7whKJKyfsx3beQIXhYZGNt6wQ5xFeN+NcZdHQng6Sg9\neAqZU+//qp3RinFuRAbdeGTgbI6U4DuRUjgeGUCTkEHQ0AljWQ3+1ghH4yJ3v89yOEcx/52AW9y9\nroKYG28TVHo1KuaTOmTNRHspgfBXNUpNnbZmIoD3BHL5LeDD/BqEofAY8C2P8psC95z2+LbI4bAa\nOi9dkRLzITo3f6NGJ7Ea43ZH4KVdUBp4X7LykyXisnfdfYUyvDEMlx+h/fMy4Tg3YXpcBDzraqZQ\npKPZAFS69mMUzR0Zc10NKfJHuPu9ReYV46VneRpyBj2M1mMiMnb3RNHz/Vwd1Oret5l183L4hYXJ\nzCajjMvrqrx3GnrGq3rxUvuvoQyVBGC8AXLKb4EU9R94cRD+dG4S8PK+7n5/xTWdkUHQ2923tRpY\nP+F46Iba0Ldb/prZQFQm+U8T+PwPUbbL4ijjclG0h3ojQ/BGd/9xrfnV+I60j3ohg3QAKs8YU2ac\nijEHI2ypppjXELIS2oQV9aK7r2s1Sl4r+NkPEG+YQvCvGHsD5MS9wd2PrTVWnXn2QrJ2FSRj58Sr\nCzKIewHfi+dfiG9Yhh11NMrSuQY5VIysnLgPkm0PhSOkFf/I3ftJSBbu7FVKZU14q5O8QGl3bp0f\nRg1eFkfOjA/ROUqd8kAZeHWd1bnxbkMO17HI2fAeGZj4mBjznTLnIWTgnsBm7r5b0c9VjLEK4rez\n0DnpjRwQ3VG2T3KONcXft7n7Hg3GPAVlNSdn8kTkRJ6OAoMvoaylmei+32v0HHNj742CnxegzJ9R\nZM0IzkLBjw28YImvmS0RY22Nqiw+Qmd7HSQTW2Le7yHZ82iRPR6yb29UybAMeo5NqMzzZFfDkVIU\nfO5BtE4XoEzvGTH2sYh/7OXuzzWStWb2cVyfaBJZsypQ1/SPkb7WhAIzVUvRwobpFHPZppZTqaRO\n0dmVzLAJ0h+X9CrNWEyO69PcvU9ZvlYxzpooKL42kk0TkLPs2nAa1rwfUxfkr5HhI7+D9nxKqpgN\nnO8lynJzvG1T4Hfo2d6LdOiBKEC+CtJbCnVqr3z+JozdA5Hd+T5wuqubZCkylXLf7u5HV3lvQ6Rv\nDSui7/8v0MKMrAWATB0NjiHDAJmN6mjznanGIE/+dhTAunD3j02Rj65kIKHpZ5/cz15IOCUhV6j7\nXI5hLY2ycv5BZvwmUM2xqIRmXBqzFuMOBcTcfU68fwNwgymicSwqI5mJslpqzek45LwYRdaZaVz8\nTIrsFNR5rbThlROEm6OIy1MoYjWZrNNO6iL2r+SIqiVA3f1hE9DlumRtg19o5HioQ6egqNFRwAch\n9Hogpf0PwLlmtosXy0Trh4T1jLiHBHju4bh7CkUxymJlNKGyxN+aukDdhRS6DiiL7Jdk3UxWo0Fb\na1fXn/9DRsn6cf1gtB/P9xKd3CroQrSXfo6eQQJKXgwpdcMo1hVmFnJ6DUNncTa6z2WQMjiHrLV8\nJzP7ZgHhnm8Y0I3qUcJkpE+lcaQzjbc7eo7jEG+YjJx4E5HTbRLwppnd5NWbQvwWrdnc5+Lu1TLi\ntkNn9OMq7zWiX8f4LyKe9RrC4WlTdqW7TzezI2gNrNwDrfP2KJPm13FtYYMqFMzLq7w1DvGwV+K6\neop1UkL3RIrVYShbcTZSEFdE+/R0MxvuDbJUEu/N3ceJSMncA619B6RzvIQMiyJOrMVQB66P0V5J\nMmBSvFKXz5TxNqmo8hpORicy7IIHpayK2aht/c+LOrGCnon73RcZ5W+jEpBngB+5Om0VbUCQ1u5Z\nFCj4k5n9ApXyTEdnaB+k0J+WPlZjuG2ATd39UDNbB2Ulf4TOb152TY6xR9cz/FydHFM3xw/N7Gy0\nZ7rEz0Vyr94EfmQZ51PI/7ORs6gJyYw9kHGxg5l9hjKyC+MouftIM/spWafFjjHf5OjrS5Z10Wjc\nNRE/G0vWsGMm4rddkQM3ZRQXdXynvXEckjenozN4D+Jn2yFHz4tEtL0E3+iE5MHGCJuvmhFkueuq\n8Y/OcY/LxFfXwnu7ADnYD25k7ObmfyFyKvZCMnZRMr0ydZVsiC+XG28m0lcWR2VRyfBNdk9nlE3U\ncMzcuiyDMjWWMmEHPY8CXOk8TKTB2UE8YXe0B7uTdXjsSYZR2Dt+DqVYE4obkbxKXTcHxX0PRZlf\nX0FZGqC9eRhylBahG9B67IecYqORPrFG/P9gFAyuSzk9bhN0v3sgR2riG0uikrEZKPv0VOB8M/uB\nNwgWxvq8BxwXzv0lgBnBp9pMLqf5zqhEdd/4dxNySE0GfuKRaVqADy2L9nBqlLRkjNM3fq6E9mja\nAzWrEYI3dkU8YRbMzVaFzMZyivOdfsB2pkDw6kgGfM3MRpHp6VMRj1wV8Z/0XaUopye8DOwdPKcn\nWaCl5nnMne3r0N7phzKWByF52wvt777UseVqTS1+HoGcTLt7RTdyM7sa+JmZPd5IH6qYbwo63YnK\n/g5FPP7byHYqS1ehzO9R6KyMQXxtAHJov0Ybmo19WWmhI2vBoDlIaV0Keb97IK9wd7LOgnOQUjee\nggDOngGGNuyoYhmoZiHGWqGcLU4G+joJMarUKW6mKTX5buBMr9K9Ke8IMYG+90XP4utIMK8blza6\nj6+jUqaWeM1BArMJCfeEYTUq7vegMoI092w+QEJyVySoZiCjZQDZWrWY2WPAMa6uN1WNI1dqdltS\nWPNjpHGXRqDa/8q9Nw2VlfwcCZei0ZkmlB58spltnRdeEVn7PzIFrrCwDAH/e7RPDkf7Jgn2Pmif\n/NSUhbJizLkqmUpwvg087+4Xxv9SqdlHZFHiUhR7Y0OEcXR7W8ZI5O67x5idkPKbnMiLIeGeDIC+\nCBeviJGfnvdgZCxNju/Id54bG9/XpdoAFXNMe+J4xH8+IlNgl0YO9u3i/9shEP7veUXqtSuN+slW\nE62+71tQVlLhTk+5MXYBmj26CFZ8V5vAh+MzqW13ovdMZVODkbFRKnoa13dEBkoXxCNSRt8TFDuH\naZ2/iZwjV1c4Gv5hZvugtVofeLfWM6g2d1d083qkuA1FSuZYd3+hxP32iJ8DkSGwCK2zflOnzybk\nKHsJ2Lfg2J1QZulpZvaK56KX4UD7IVmJZpnul88Cz5qakXRHpXqzK64pTC4Q8INRKfSlZEGUrug5\nXEl0xqvjMOiV+31tZMh+Rob714z2z+SY88XAFUX3fNxTKnGeL2Rm5yI5cB4KriVsL9B+XJkMILww\nxTNq+Jla65SeR8iEC8MIHJh79UR888nkBC275qhs5nR3v96UnXydC2rgRFNnqkcpWTbiWfe6vwMr\nmFl3d59ecY0ThnElRdBvc1MJ6RrAR6Zus1OQ06gJGbtD0H4rlHmY++47ar0XRmDvyvk2oL3IHEVJ\nLuYDrT3rGc01qA/ira+jTPRV4/+pO3dvxC/3rhNgbEHO94YUcr1Do+tcmfBvxWdSp9kuZI1eknO5\nB9qjhTLnY+wZZvZrxFs3QffcATnGrvTiDVUSMHvSa++O55+6F08ws3OQkX4Fcvbdh/ZaXUdWOl+h\n14Ge75xw9syq50itR8H3h5vZYah0cQXEd0cBfy2zf+LaVEJa6mzUoGaUjXs48Ki3IUM1R/1R4Hhb\ntL8NOV3SmZ6IdL7uyA65ID7X1vLmrsgGWwGdxc+BpzyDdagrb929ppPPBLLez8sHH9P3rYaqNkbF\n+UvVPrORs/1RdM4bzjP24zrIAb4a0vtXI8N3vqHkHBP9DjlCj0XBrI9i/quiM79bW3TVLystdGQt\nAOTuk0w1uZdEdGFjlGbcjSwK1C8uH0GBdq1mtg0CZ77OhPOzGllac3pNQ8pxU1lBkmMQc4CHkFPj\nOiQAuyDj9yDEYB9AYJZDzKyVF92ydNEdkMG2LBKMi6HD/xCK/LxL4wyO75ApQesjo+JBlKXTOcZe\nBwm8zymJ0ZNTfPqiiMdtcc+jkRKyHvLivx/3fBLyyu/p7iMrxhqIFIGhSOCNQyVDnyMhP8pLZBOF\ncHg07q+aY6wLwnQolL3gioyfgjJAPjSzZ9DzX4QsKnVAXF62Q9xsM7sJZRItj5wFc1Cb4nyp2QGW\n69BUhbZC5Vape8/KqCQgdVT5rZkd2gaB0RsZZqn7T6UzyBEuTMMzk4tuzSbrPtZeSvfzKQKIHopK\nEfL3uQJyMNfNaIs5pn19LtpD5+X3a+yta+K9v6DyvRPN7CVvkBpdw3l7VqM51Rnv7ZjTMihaPxs9\nhw9zhuB8IVe21iRUYnExNfCNKsmUBXkcyqTqQlaWOQ5F4RLQ9MMNFK30/47kzpi17vg0FukJdQ3I\nUE7XA97yXDlEjPEhVfZJQcfQ+0hepXKrPkjpTk7avkix7oei3ImnNcz8dffJZnYiAZwagYH30Rps\nimRMXawOM/s6ygp+3VS21gM9q+noLI4GOlgbS+Fyc33fzPZD524tJL+agce9ftfVxCOuJdqZu/tV\nJgyifug5DorxUlngymSGdqEggpl9Ezn+hiKZPwbJm0/R3nzUC5ayx3gdUZnQse6e8GBayKLMj6BM\nrcKZzzkjahACgB6I7jO1kk+Z1qPq8XTLSvTuRY7Qk1y4laVLzCspdya6A2+EU6IT0CV3lk9H5/tC\nCgaOzGwXQkajQNn3gF3N7C6irL2AUb4CcCTMxSj9Kmq8kMpoJ6L1WTKuT5hoZbuyLhFznU6GezOL\nBoGJkOU9Ev8Jp9d0xMPaRbl1eQVl0nZFunPiQX2RzjKEBhnKZpa6NKdgWCqHShAACQNtalt4Rsx1\ndrxKOXnrjDkHOejubs8w8bMLOnvVeMs4tL8Go8zLTtRwrObJzNZF2Vxp700mKyOdbGbvuvufikzS\nzAZ4dGxN6x56yOfIXmgXhb43lAxSYQ5aq+TU8wbOkcQHhqFmDSuY2T+Q/pQwi8cjXvapV89ub0Wu\nbLbtYvzfo6ysw5FsGIrWYzDSXa9CDrTC2IS57/HQX85EGW6L5N6eaGaXo2ztQg5CU1Z1D6RLN6Mg\n5EzagA2V4/kjkcP+iirnr2/MOWUjV6v+SfLhCJQw4mS89xlUFfIMkl2lM9rieyeZyrufQpURyyMb\n9EaED/wfh8j5ImmhI2sBoRxjuA+VKYz19uGObIMYyHUIR+QYlNqcoispajwegTNf7e5PlRg/GXU7\nICfZvt46S+KfZvYiSvl9EaU9XxXXVyu52Qul7r6M6vrvcAFXF6ZgauOAcWZ2anzfcZ7DsDKVQt4B\nnOjla/JTxOpApCid7VkEcgpwjwmg/3SkjB6MsL02Bv6SU9SXRm2Rv0VmQCZFK0XmHqJGi9sa1IIU\ni8PMrAnto3Ex1ioognwxlMpeuMfUNngvZJwtjZj+GyjS93jRsaqM7SibYniD6+op2esjhSi1ij4Q\nOTe2RsrWEfF7Q+XOlLK9NJkj8VjgUDN7s+w+rJh/q2djZsuhiOfi6By+BvzDq2Qq1hkzPZMrUfr/\nFWZ2PjLym9AanYsUpiIAzmmOWyGMk5G5SLO5Wjyfg/bU39CZvpUC2V7zm8IBfAFypjq63ymo6cKF\nXpFqXmecdBbXQQ7oT8kc/DMQn9wIncFbS07zCITvdwtSZAYgA2oIMhDWIov01XSO5ZTQO9B6vmYC\nM50Z99ALOXLmkBnp9c7iXch5fr+ZDUeBktTwYyza+5+SAYDfX0YRDgN1Sny+0bVFM3+fMrMd0T5f\nH+GrdEZ7/QB3v6UBDzoHZbgchfjfd5EczDsWR6Es3Sbg1qJ7qMpcJ6FSpqIt5NPnPBT+FgQE7Z5l\nU9d9lvXWJxckOgwFVUaiQNgAlCnYC53hYei5PmbFsxoXRTLw2fiupZDSPyWcO11Qhk5R/Jd0HpdC\nTqDBKDPN0JnMY4IehHherZK49J390Tp30JRaZc6kYERp2RXnbiSS2V2Qcbq+ZziFA1BnvzLg5acg\n3jADncelUNZLKqGeFE71CchxcKnPm611FzrjxLVXoPLJoYj3DEZyZzjCVHoxPld0jYYifrMiMuxb\niHNkZjMRnEK95i+ro2zrfWI+h6DgWHK0Tcm9ZqAypoY4UeF4mO0CkZ6D1qYh1dnn66Cg6GQykPSU\nWZoy26bFM14EgbS/WG2gKnPNN+XZMsacgnjvJwQ2mNfJzDZlxB+H9J3eSM/8hAy/M/+a4sWDl+ks\n3YT2xzmmbMMPUTBlCCpHn4N0rmXQmarKo3L3uiLS+ZdAWUS9kGN+TeSsT1AQDR1Zpsy/p0ywKcPJ\n9NxUvj6WzEE2GgVvCwWE47legbJmPkNnO2HBTQPed/fzi4wVtCgK7LyPzvb3EN8xpF/0RsHwnxbl\nu8Fb9wW6VZ7/9lJOjp6CdNRT0ZpMQmu9KyrDH2lmlzUIJvRH+3MFdGZSkGOcmc0ARngbweiRTL8Z\nlbXeQYY11xfJjuep7yhryf18GFURPOfudW2RehTr0oqXhj5wMxlUygJLCx1ZCwjllLLNUWbS1cCr\n+QMSUY0BKCW+blqmu//EsmyWK1BkcnEkNAaRpdkvjRxI98Q8iiqyyVOdUrc/ic/nD/SbSBFYw93P\nNLPPkdKcn2f6rlOAN4sK3boTU8RwZ2BNF05UV6Lc0N1HhNF/EhIiZSgxqSWRklVNkLyH7nFxd/9r\nRKm7xnvJaN0cGWWHopbloOeUohcDKFgWlxM+a5F1qzsHGdNjY9yVkBC5J7IGJprZh16gS5W7v2HC\nfemFjImZHtGwEnulcs4rI6GY8C9SdsDHSBl52Rt0wwlaDAnECeF42QyBat9v6v55ANrjRWhD5DxM\nCvostBaPmIAbP0ZGfsLbeMdLdM4KZftA5ASagM5LX5R5MNLMTi4r2GMvH4Twdy5DSlcLOpNNwI5F\nhHNO+I5BUb8/V4l0rRTzTWns3Zk/2WWFyZQGfhFyTl6AlP8eaO8fCGxhwhgrkrKezuJuqIwrYVY0\nkWHqDUBKUVKwi+71bVHk7egCfLqIo+gKlIVzCrBX7McpSLlcGznOh8d49YzSg8mwM65DRkRfZPAP\nROexF3Jw9UL3XzZTtysqJ/s2MgT+5u43mLJsOtEgm6YaufvzwPOmjKpU/jgu9369e06lGKC98yi6\n3yFIHi6Pzn6P+PslCkaLc4baoSjb91fuPs5UynYUWrO/A1d5DVDgHB2O1uATMxtPdIMjw6lrIsN3\nakadNIs+x/1RJuWvyXBU8tg/A+O+CzsYkVwZgbJfXo7xmlGGgUcA5EMoLCfSeTwM6Sf7I8y9dP62\nRxlefyPkdq2zk/uuW1EQ6fz43/wq5WhBxv7i4eS/GQWQpsR9HEZ0vywR6NmJLPtuSWTkpizGIYin\n90BnqieNO9MOJct+aDPl9vhgpFesg4z8jVBG9SZoP81GWej1HFkDUPl8cm4cjeR+J7KMl+mE8xHp\npKcWeIY/QTL1D2a2JcpEG0mGL5c6DU9F52hCA757EnJ690brMSBeiyLeke8i+hUKlBYminv/BllH\n6PXB3vX+AAAgAElEQVTivud2b4a5WUe1yhsHob3XHI7fn5E9x9TAaSbiG2ZmT7n78SXmeLWpWclx\nKPDxKVqX/mitT3ZloB6MMtdH1BgqZd1uiJxY3/ca3X+T3VCAuiKdOWEGd0F64LLxXsIATJmS45CM\nrEumDO/LEX+9CTlZL0N7aY+47Ma4thCmnLs/Y2psk0pnU5ONBCexFMXw1TCz7YDh7v522Dct4dSb\nSTtKM6vNG+lEJ7p7nse8bGavI8ftASjjq2oiQAQbT0OB0Uko0eFjdIYS9uFTiF+0he5HWVMHI/k+\nBT3jFdB+3K9eECF3n5d5ncyyErwbxAc3RjAIy6FEkpFk/GxK/D4FnctpbbGdvqy00JG14NE+6PB/\nCnMFX2LyA1BEbDwNor5m9irKSLoYGXrPep1Is7XG1ylC6brXkRK2n5n9rkJxWg8ZwNfH350I4N5K\ncvcXTbQGUgwmIaW4LYpYd6SwbEeFcyzucwgSKGUp3fO9CCfoCKRkT457mIGy3wYifIpe6J4rI0LL\nI+X/mhKMsirlPv8aUi67ISV2GHJa9kfOtSUQyOAiyBh8BHXqqkshlNaKccaQw25ooxNrW+SE6IlA\nVTvH+D3j98HIKXFlI4UBGZxfNbPeKNNvUbLU8p7ovBQtW3gNOZU6xzj9YowhSFHaIMZLGAS3Ad+z\nBiVJ1rpBwHFov5yPFLwE2H0MUsJ3CKO9EMXzucPUnn5zZDh3QgrXNV6w61GOTgJuDEP8VmSIGlK4\nTkYAxDMimjyqoMOo3ZR7hmshhWFfd/9rxTWbAH9GDtxfNRoz7St3/5mZnYzWfHDu1Qed29tcgLJl\nlJpJwMeuLoUJCyV91vW1pbA73MxOQIrft1CGZT+kpO/r7n8pOM5fc7+fGs7VBKLdjcyx0QtFfAtl\nlOSM3aGonHtdFGHfASnaN6Dsgy2RM6505DMcZOOR/LCYe0u9sxf3mZzu5u6PI4D4auMnB0FbcPX2\nQ+c6OatOQlmszyHn0TQTdEC9/bMxCm50RmuQHFagZzgRORBGoL15YPxdk3L82VHpYMMy46Lkavv+\ne5S1MRXxicnAShFw2xmV1pWlLRHe1EvBh952lWY+a2bNZIGzItSMYAruNpVqvktrSIXmggGTVhTn\n+rdoHUB61ldQ6VQ3FOE/KK4txDPc/U2KQUb0RfgydbP1PesauwhyVidsrVTSNqvR2UlfGZ/dFJ3r\nLRAf7ufu64c+dQWSEQfWmbe5+8MoAwJXo5tBiI8lvMh8+ewKZCVrjUq6VyNrgPQjFCRLQZ2ZZEDY\nExC/Owp4oRZP9yyztCGZWU/Klc92QLL0dRSAeA45i5pRqe5KwLl1nFjJQbJ9/PkvYEe07/LOtgFo\nfw6rOkhjughlT22MMuk6I954j7s/HdfcjSonGp2h/mh9XoG55WZz0L6aQ4nMyFibuU0QTJ2t+6N7\nTRikeUdjIUwn5GxbDGXibYae68+QLDwCOcoOjTmUyVJ2Midq1cywmFsjXfp0VN3xNnLkrUuUZQLj\nTbh4o5DzZBrwey9RKp6fC7rnalADs83sryjIMI/ul9PTNo3X9ugcH+Pua5sy3s5B/Ge/snPLzWMG\nalz1MLIxl0XP4UVXFUkhp2g4gjuhZ9kxxpiAgkTTS9pmqyK+CLr3C1CwOgVHkxNrAtLR/wT8uaRe\n+aWlhY6sBY+WRYrRXCaU2+jvogPTtcrnKmkAmYA9D6UE1wSuK+uUyF1/HWIExwFfN7PnkcBbDnnu\nXwfuMrVM7YcY8TwUEb+jkfGzBNGa18zuQ4ywDL7FJwhs7/RQNJ5FBtB0JJiPQBHqUpRbh98jZ9QR\nMd83gRkmLLItULT0eRQxaCbrGJOe2R0om2IN5NBqNwVzrxrtShRGdXek2NZd74j2nI0cq53I9uME\nE0D0pV6idW6O9kalGMei59Ij5tSTrFvcU3FPjRSG89CzfAE5nC4lw7/4JhKY84CCVyMX6P+tMFeY\np+5JHZHzrwtSqHsgpTthwTSaYxKq/w/tyxMrFL+348w8ijIOni/gwEtzTs6YVygY2WtAtyDl4yi0\ntzuiZ9gfrclBphLMtZDT6Iui9AyXRYrAAzDXuZFArJ9CZQbrVhugHrlSwCeRdW6tdk0ZZeNA4Coz\nu9/V+afNJeLJ8e4qb73bzB5ASvoUz0oMi7Q+74Kyb8cBU919pmdAvqXbnldQMjSPQtkkh7v7zWb2\nFJmz5Q2UObEsMLzgnC0+szviEQnXKnXFbTazG1xAyrXGGIBKdRo5vJopmWGYm/+yqDShOTIZ9key\n7LfIcXcIkhk15+Du2+TmvCIKljyLMroMyZvNUbT8A8qBtp+F+PhNJT5ThK5A/PAnyAGxCOrw1Au1\nHr8YCusW6ZpFyErDutAao+RGlOnUvdFgsd9TV6xhKEOwmazz4wRkCG5TdYAGFDx8bPz+MbBb6Bot\n3oaSn5wzuAvaT46Mf0Pnq6sLkymVjDUabxXkSFwOnc0pZGVXY5FRXQSrMD3/VVD284dmdiDSA3q6\n+1QzOws9622p0eErd29zYTRyz7ARvEC98llz971z/zoc6S0pMLE4mZOsD3K4NdJ9dkLYdmMjaJMa\nLaSsyIT1M7sNOtBApAtsiJzSnVGzmhHA7WZ2d8y5LqUgQzh2His5h4YUz/zdeNW6plFH6fScf49k\nz07ISd2ukjjLMI5WBb7jwtysGiC3DE+ykfxeCcmJjyIgMwEFcyaZ2WUII/QA4EwrWYkQZ3EtdI4n\nIP6T4Ave9WIBwW3IgixHIp7WH9lKyRG8OrJvlgf+Stsy5rsh5+oBoWs0Vzy7b6NyzWpnMvGKNVAD\nlZdN2H9TTI0r3jPhRp2BAhaFbTDLyuRXR/L1GBee7kuV1wavKaJfDEIOp6URz20iqjFMWdEdgC28\nAQRNfNelyP4A6cbPIxt8MFm388WQ7bV8/ISCuKtfdlroyFpwKDHGScDKNQ5h73gV6bQ3AtjJzN5H\nwntgeMRnIGGcyhRmlXVi5SkY/VEogvEd4BvI0TYTKTXnuPtoEy7UCVTJJIsIzclISb8MOWQSWOm+\nCNNlc6/SqazGnGYGw5yBnAPfi/EWRYrE9Sh62iZygRAfjZjolgivpxty0HwfuDOY6WPIsZKA/VJ0\nczASnCeb2TVIqUwt66e2R9BHxHZjdK+TUEQ7GXotjaKNOSF9PupIdTpyEKRMr81QxksHMzu7THQq\naCngL+7+j5Kfm4fc/Z1QOjdDSsGNnmUhfQMZgaVBFePs5Tt8peh2P5SJ9E7FtXWHi58zySlb4ShM\nCukkZLzVNbZD8N6KnKdNCGctlWOOo7WhMqGggpRNVGt5p5m9gSJpQxFfestblwQcZvVB+Oc3pWeY\ncBDWQRkm+UzLvki5+2Dej89LOcPxe0jOTiRL+84bLNPi99kl9vrDyKFzuykz9j3keBkZP8eE87EI\nDQPeDz5xiQuLZWzcQ2dgTkH+vSpSbkegTrKJDyQnXgIzHo+ix58U5bc52hQ5Q1Pm1wAyWTUaGWgN\nM2xzjtwjUQnBP1AGaer2uTxyWK6MHLhvVXP+RjbKGKSYTiPr7DSKbD0+jZ+j0LqUwsMLx9V0so6X\n30b79bIwth5H3XEbne0OMBdE+ApUovWb/Bk2lan8GrjQc6WVBehGYD0T4PAj6J7HkwGpj/finc3m\nUjgxzkFZbiuh9e0C3OvqYFpmrHTG30eGBSjIs00Y95+jrNju1AEVT+c6nCUdwnmSDL7UgXQIkkPd\n858pM9/43KbIIdEFZbzfZ2ZdzWwRL5lJnvv+IUhPusdVRtQJZdxcGL9f4u6nNJjXIBRgHIB0sDlI\n70nPYF2kG51V4t6rPffuiFeMRs+4UfbPLiiD+kO091J2XCXfnRWvyY14bqXRWtTRlz5b+b8I9t2M\nnE1j0XlJ52QcGa7eODMbhwIKNTsrV6FF0b0m0P2pRIe1oDuR47tuVnHOsP862h83uPvzOcfNbFNF\nwGxvA9auCfB7E8Rzk5xImSVjvQCOYI4nH4Ian2xrqrh4E/HfUWjvTCij8+b2xNeB3U3ZkdNR4A3E\nf2ejQPIywM8LOJ86kulnHZBO0B2t1WQUaE04fUUbbHRDmTfbkD27nmiPv4MC+/sCzzQ6h+7+Se73\nv1e7PtasG62DAaXI1eDmgpj3fcANZvYRcgLvhKpcjm0wTB+0rsRcZpE1h/gw3l+h7NTi51rILjka\n5pGbhN33dXffosCa/wrxwgtR5dLFKAizHVr/FymQmZn77rQmywIjXUHMRp/9n3diwUJH1gJDOaZ0\nLQKxuxu1aZ+KjJU5iIF8RAEwXZQhdS3KXpiNHEU/RUI5L5DHmkArL2mjMtfB3UcjL/sZwUybfd6y\nlH96BSZTxcHfBdjD3fPAeLeZ2R9RCvPhMf8ic1oGCZtTEabGGkiBGI8M4MLYRjXG7xAOoaviVZXc\n/Y0ab6WOTCuhcsDPyFLgJ4cAPNBLgg+aSh2PQsJsAgHGamZ/Ai73xjgtedoZdS45v2It/2oCRT4C\n7a+yAvMmVAowX8jdX6d6q+SjEZ5XYQUppxweiByUJ7v7K2a2GCoV+TYyms/xguDsOUF6KYqYHWVm\np7uygJIzYle0NxuB0ndDht40tJ+/iQRtdyQrOpCVkHQwszfcfcdid99qzu/TIJOtPc7vspT7rjtR\nydZNZnYGMnSnon1+CFKOGmHHVNJVyJCYjnhGEzJakpNjDtHdLpwhV3gdHL9Yz9TNaTA658PIMg+7\no/Upit32OeJ7ewEPRZT0D+5+fxUeW4+agafRsxqAHELLoKBDZzKcl1mUB6FN788iaw3uaI8mR1bq\nvDt6nk/Xph+iqO0JrozJVhTPugVqKoSO8E26o+e9KFn0egNkpPVG56oLMlj6VhmnHnVE/OcnJjzE\no5AjZ07Mb6kYty5WVPp/OMvXJ/DVQlE3FIR43dTt9Sq0dg0p5PGp6HyktU9rNDvm/z6wdVscOmEk\n1yzZbAOdT3Y2LkUG1ZXoTG6JSplqYbMMQ0brcDLnfnp9ALxS+fzbcs/hvDwZBRRGI6fHXcjw2wYZ\n7L8uo2fk9sZXkXMsgUqvH78/jYzAvczsZXe/vXLuOcfB8sgZuH0th6JlUBKN7j09r7eBpUz4PE+g\noOMPUQnaj1HG1kUNxtocZVfORGeuicBxiv9NICuf7YVwdqrJ9lZUwyG1PSrb74ACCa8VkdlxbocB\nn4dT6DR0ZpZE/HxVtD97k3Vja+jIyq1VF6S/r4z25EgUcH4D8aa1KdbJMDlTdkX6c2qgNCf33o+A\nPc3s8Erdu8Fcd0C6naH16Ryvj5G+8QpwYIGzk/ZOd5Q13w3tmc6I73RGPKifmW3m7o8VmFsXoFc4\n8hdH2UGJH8yuuHYjMhs6NWqqRW8Da5vZ8ugcH4Ccx9egTMPlUeAQqNtQJX+Wz0EZkWsiO+x2lGW7\nFdKv/0mNTLJGFA7c7qikuRuCMhhFlZK/Nox9uylAfDrSpxKG3SsI/+/aWh+Nnx+iTo2LoSDUTijJ\n4XqkQ6+AspULUQSkBpvZSCT3/gV0ysnG5EhKsDEpiFBzzU2Z2jsj3LZHzOxi4Ex3/8yEO/cgwtst\nIxtSdtUlKCD8G7KMWmIunZDz8t722qFfJlroyFrw6M9IYbselRu9i1o7b4WU4m29WDrz44iBroOY\naEqzT91rViPANxFfvLgtk80p4Esi4TQdMZmmfFSyhqGRmNBSyCB9PMbqhBT3FiQ870PRl7qUU+QO\nRorGT939BSRE5xuFs6M3UmoSNkh6TanlZc8p0aeh6Gjv+Jkwmfrm/lcI9D7nfNkfOS9vRhkRHWOc\nTVAJQScUzW94b/HrOJT2PCvWw4AOsabXIoHWlpKpscC6ZnYlyi5KNf1N8Wou6XCrSl4HY6IOJQVw\ne+TESGVRh6Ka/qfjvc5mdmhyRtUdMMPQugXhG7Ugo3E4ehYDkJD/DeqcNhDto3n2kKusY69Qtj9H\nCkJXMmDghA3RC619qWy0MKJ/gDIMUzfAD5FhMQ1lZrUp0tdeCqV5oglg9hS0/1LJx2C0v49299vr\nDDOXcgrKt5CD7F6kdKXmCN9CWX0vkZ3NvlTvuJofd5aZHY6U9y4xr24xRq8Yo2PtEeYZb4aptOFv\nSAHeE/itqWzvGlSW0rA0MJzqu+f/FzwsgRf3R3yoLzLaXi0xx/Qsb0VlZo8hY7crmSG6NzIoP6n4\nTLXx0t434AUXRlkH9NzyuE+zG4zTTBiZwcM6EcC4MV43dHb6xr2X1rfcfbyZnYLAb7+LMo4TsPJg\nZEyWyT7tjs7/lqibaaUivkzMu65jLGdkroFKXY9GWV6D0Xon2T+YLOo81ygoQqYSnKSXzCQr+xyF\nMjiGe4nMJDMzlAGTMh8eRgGTXZEz+GJkXNWiIcjhOxXxwW5kGT6zgakmLJkR8b/n3P03RZ1ZOf3i\nQGTcXoWM1TvJGgS8j4zXrwLvFnQEQyZ7lkbOsZRFvSVy8PwfMoiWRsbg7VSUpeTOTa+4x/dj3l1o\nfW7SqyHlnstdSFbPceHQ3AucZ2bnonNzFVq7emPthdYn6Yt3oH1yP1qfpVFgb3Mkf86Oaws7G02l\nR9eic/IhWYBngpmdiXAe666H57JfUOlRve/rU+/93Jhp/h8RRnzI89sQz1wa7fHVKYDxmKN10Nq8\nm/ue9F33I5ynfo0GyemRG6IzdibiCychJ/hqZN2W/xYfq1sWlbvncxCvSRAN3WmtqwylBuRIFVoD\n4fKtEGN57MXPyTrvfoT421fJnC6N9s+NyP4Y6+7DY1/viZwSvRF/fyzuq9F5Tt+1JXCRK7OyO/Cg\nC9P0ZiTLHiOA8ss4TEwZl+fF+Isgm2u0mT0E/M7rlNoXHN/c/X5UCZNwaw2d/ZoZy7nnchNy0rag\nvfID1GX2DLTWt6NqiaK0OuL9qyF9z1CAaxTRodLMPkXnZ1OkF0H9NV8s5veGqfRzBtqPnyEd5Sq0\n7/9Wc4Qc5ZxqIP4/3qtkYZvgMM5GQdh32xJI+TLSQkfWAkausrXdUAbSdxCY3QxkGPwwHDNFxnEk\nvB8HjnX3M/8d8zWVO/wMRZhmxVwnonbRk5FgqIXFkA5wqk3eCfhtBQPogxTUMi3Rh8VckpOtKxmo\nZLtSOYOx74AU7AFIiLSg+54GzDZlwvyiFpPy2llEbZpS/NwHMfdjKhws15jZGGB/M7vVC5SQhEFx\nGfALM7ujijHyYxQZL+UsCmPy9/HnykipTSWV+UzBXcuM+2+glZCSnbCs9kQZbSeZ2WZIUA6hADB0\nbi//CUXj8gDyKSPmfaTMHoX2aUczW9urpCZ7hovVTAlnQy1Ke9SEUXc5ivAlHLBPUCQy0YmoK0sh\nDK/5QSZshbnAm66o+g9MJQqrIv4wHoGylyrnMWHaXAKcizJSZ+XeWx+V+PzKowzWGgD7J/KsXDQ5\nB2d5+TK9/HgtyLH6ezN7BGVs7IUcE3ea2XWo02fN+48zbeTS8MMBNpnaXafKZt5dgJx/fwtHW3dg\nDzPbC2XcHtbI+RuR0imxvw8Djjezv8Rc21MCPxvx5s1ibacSoPzIQH+f4o0hKsd+LPbj4Jh7ciB3\nQ/g/SRkuorCORqWFJ5syQ+5ExllPpKQfiIyqRpQvYx8BXOvKYqhZlldkrXP8YhuUKZT4BMioStH7\nwai76JNFlfW4Zi5WWfz9J7KuoY0+/4xl3TF7IZ66JwrsvIvk9QYIagAy/J+EBdiIkqzdDjkJzgqn\naH+ybqCfI97eVuoe82mJcbcEHnJl53WL91KEP9/lrh/i26ORkfgEwpI52Us0lqhGsX6pYy8A7r5/\n8J0VEf7dy96ghC14UMc4i+fEPM+OsdM1y6J9db2rjLqwkR9OpWvQc/k+csR0RIblj5CjfVu0dvXG\nyePxbOXuZ8ezT/LBY91Tk4zCDSJcXU1vQAETUFnTHHRWZiAeeX2tz+eHyv3e3bOAcqc0P3QOlkC6\nVSNKe2k9pNOehQLCH7r7TSgL+n0U4Lku7qWQDuAZBiUxx67e9u7ko1Cwtj/KmmpGz29ZFLhPa9IX\nOaZSlnsj5+UsWgf+LkZNG5ZDAd3CTXhy+7UfrfHfOliUHZvZpSjr+QYKNhYAMLPFUbLDqsgh8h7i\na2uhDKMNzWwnd6/bCKTR/E1QDYmXf5yXDY34eci/j3LX74bKX1dBds+9jfSACnoH6Z490boYemZL\no9LA/oj39UQ2UDo/86x5bu49Ea9OGdhjkcPs7bjv1MW4KG2Fmj9NjDG/LnY3176ZFL9/FZ2vj6Gc\nA/PLTAsdWQsguXAxTicXfWyH8TgbdXhIWQBJYM2NzLX1MJnZooixrI4M/45kLaN7kXW6q4rFkPs7\nYXecEI6ih5FC1htFhDZF3RrrUu75/BIpSpsCD7RDaM6l3Py/ijKbxiCvfReyFva9kQLfKz5WM2IV\nEcnvIYY6EUWExiOAyTIYWekZDkUA95Nt3gyqK4kuSgWpL1IElwLeMZUzjYj7WQ5FTI83peBOR1gW\nDZ0ILtyGFI0bjPbJUDLsjqXi1WbcknZSEnzNKNujxcw2Rs8jCcdP4u+yIK/Xob2Sfy1CBnSffk8Z\neVWBx3OG5IrIyHnS1fGzA1IU1kVRpUcLGjBpjx6EslJ2QRFhR/xnLZSR9gZZs4gvrKwQuNXMhqDz\nNhmdlU9RtP1T9JymAMPMbLK7l0nVH4zO836uTKqOgLlAfP9hZg+iM/XV4L9FjN2UqXICUvo7o2YX\nhyEesSLwkhfIoqpB48icvoORMv/j+I5T3b1qlC/+zpchDURldzsgxf8zhE14o7vXBL2vRy7MpL2Q\nI3pr5LjdGq3dEe5et0FA8K1nUebMdMRb1kaR4SdRlDfhwI1DZSVVO0FVGbsHWpPvk2W2jKV199o/\nIsdHo7EWQ7z1s2T0hlO/lcEYTtcDcn8XcebMMrPTyUpKd0TnM8mE64iMjQaOpzzm1NuIL9zT6PsL\nUOIXRyIH4AFoLRJ+Z7/4OZTgYfXu28z+H+Iz7yOeOpEMq20iOvPJEJiMZE2959js7k2m0pj1gT3d\n/cHc9/VBDtKvkGXGFDrXZHyvc8wx6VKDyEpmu6A9lZyiRTOfkp7wCDrPF5GVvN4Z7w2OsVMZZ37s\nfRD0xAiy7IIhZrYeCngkPKLRiF++4wW72oa8uRjpaU94gHy7+xPIYVaIYt3Ss94aQUl8Gg6uVOr4\nXhj5V5uaZTTkkzl+txTir1t665LKp4E/hh5zPOInRTLllgWONLMHXV005+rgwauORbrv9vUGqaS8\nfucqBzueLIuz6Bhp7g+g8sE73f3Jir28K1rrMpnZSwGjQ/dZEmGB9Y29cjPKrjkQde0ubJOYyhV3\nRrrTJDM7IdZ6yfi+QkEoV0bQpTHmdCQTnkG6U8pO74uCys8l3b8R7zVhjS2N5F/q6Pk8ar4z0Mx6\neYlKAVMW5PtAj9DNPgW+6e63xSUrAj2Kjpnbr2uirLTd3f2eimtuR5h4v6Q4LMA834P0loORnt8d\nNVUZjgJVVxd4lob0qFT2P4N2NAcKHpAa/MxBcv/J+Ds1YVoE8b3Zue+tlkSQ/jceZcR9BVU+vQSc\naIJNWRXt8TINwfojXTLtw2+joF4LWQfiZrTHnqSNGGZfVlroyFoAKZjqaqgWfwQCciyDhzKXKpSH\n+UI5xWFFpCBv7+5V08pNgJONSkmmmNnJSDE7FTmhUinJSOCX7t4wKptj3Cci5WIlM7sHGeGV4JJl\nnVtJgU+tWnfxGmm2lgFu1nJibYWyQFKr26GIUU8Hfmdmt7j7LUUmlRNUrwE/cvermHe9N0bPtig+\nTcJ/+BdSnDdBgOoDY74fIcfHwUR6Lqr7LjLfJrQWnyGMgFrXfeGRitx3PgAcHRGVg1HkOBn3KyLB\nVAbrJ42dmiy0h9I+3BF1yHwq/r8OMszWjr8vMrMjCigyyRjbCOHYvRnR/Rc8usLEfv5uuvYLXpsn\nkPO0J9p7yyLjIYGaJgf9TKC7ma3uDdpO5/hXV6RcbAq8UeW8LkeJMsAYexWUzTgYnfFdgPVcHe0S\nzsbZwI1FnbVm9pUYZ0tU9jcLKcYXIyyHfohv3mFmP3T3qpl6OSfosshxvhp6vh+j6ONhwLfM7Ke1\nxmhE4Vg628yuRZHiacnwLXC/XcjwxfqiNX8JZTB+J67pjPZhJ+QsWKfefHLG1g7IeD4elc5+DTgG\nZSDsjoyOKwre5kHIUfAz1K1uV2RQTSVzwiQw9SbgTW/con4uuSL2VyDH0zLo/jsBr3vxzICUkWVI\nuT4j9t9LyCGUHIWzkDwslLWTOyOrIudke7Gx+qH7S2DKeby2FiTLZiLDtCsqWzm/jpGWyk83Rc6v\nJ2CucYWrOc3lqNR7B+CyogZf7pr7UKnffchJ0hPJSpDeMY42lAzF9c+Zypp+gmTwaWRlON9BsjnJ\nzfzYbyInp6Fn2gOdjyFk+yeVGA5FDsjrSxi7K6Mzs284lR+Mex4bztcy5X+d0NnYHGXSOq0DfoOA\ngUWcWBXUE/GyMfE9qattamj0d+SIgVw2WyVVOIkeAi43s+3D6ZSyYn6NZPDhRSeX4799UFZtf3QG\nJ6C1GgdMLKmbXoj42p/M7K8o4yV1594fyYUiukpau+lkYPkT0N4ZEP/rj/jRA0UnF46RQxDffQk5\nG36IOop3Qhl0TyGdvSxdWMIJ3YjOQU2a8lnZ6WzshTqoX1ziPBsqoVvd3W8xs6uQTrYE4r87EJm6\nJYO2g5D+nbLEFwHJDHd/wFRmuVJc2wgXLJtsdq/fR3rFJ8gh9gnS+7dBjqzBZnZWPXkR99Lqfkre\nYz26C2GqdXT3OaFX9SArfSx0v66A37FAH1fg42ykm92IZMgN6OwUIlcW5fWmjODHUSn/dPTs0qsX\nco5dXVTe/q/QQkfWAkRxIA9Gkdg5KGPlWXf/owm48EiUiv1MnWG+CEpKcj+ksL0Fc5lqS3q5qGHE\nIZjoCARkegqKOnRFDHu4VwH6rUY5JtYBCdtBKCplSInrjJ5rHzNbwstlb+SZ8L8Ih0QI47n4E5jy\nD1sAACAASURBVEE1haupXOQMlFHycyIKhBQFRwbqD5CiXYZOBp42s+eQsv8uWQbVsSjSVKgUMAzP\n9UIJ6YvWuVvMbxCK2i1P5oQr1ebXzJZGUedhKFvgcuSUGIja9v6nmfyJ6N72QI7UY1wRytRB6vk2\nOEIxsy1QFp4hhe5TdH4moI5prxUZJn5uiIznVCr6MySA14z3foZKKRphEaR93ZUME6wbKsNKysdt\n6JkM5guu63f302GuIdoBneF89lpfpGgPivkVKfdMcx+OIoUXmTLc7ifLLtkW7dELS055Z2RQ7evq\nLrQMcryBjIxP0JmE+hmbnVwZjDcgfvAmyla6OcZ5pkLpfgIZBKtSu+Q08e1fx3dvhM5f55jzaqhc\n8UgzO6QI7859f09k5EyN14TEXyNqmhqW1CRXpsJP4zMdY07dkFGeMMoStsqiRKlPg/2YzssmiLfc\naGbfAEa4+0MIQP9DdNaLAuUm/A/Iskv7xDwTJg/ISToIyfRbijgN0rqHDPmAgl04qw0VP1PjCpCi\nPgvd5yRk8HdBHXwfbHSuK96/F0Wz20VhfK2G1jphtSW8tgG5V+/4vlS+VssRkZ7vDJS5MBCVxuTv\nqxuSYUnO1HRq1KALkRPm2nAe9AfWirN+InJUtzni7u5/pHr56MtorV6I61pyn7mHyLgLuZ2aSyRc\nvj7xsxdyhqdSyKJ8/AAEar8FkoF7Iz70hJnd6wUhL4JaEMbPhSacvluQLFwE8aATKZE9mFvb55AB\neqKZ/Z+37mq7HjLwb2o0Xsiajq5yzqOQQ+IiM9sD8fIL0f7cwQsAlKcxw4m1EXJOLocM3Tz23wxg\ngJnt6AW7Ors6gu+Fzvl3kYO1G5Jjpyb5WWCctJeeAIaG0/t6hA16hwnLayskK55OHysw9BBkt1zr\n7keFI2drsmZE91Ayoy0359lm9k2UMT4UOb3fQzrPPV4Qz9NURrcaggvJj98Se2E6cKi7N2pmkP/s\nTDJnHe5+iQlq5QdITt5Olllb5Dmma15GjsltgT96LpMt+M9AsizJMtlYyel1CNIzfuzRMTdk8e9Q\nYG4/5Ex6qcTY7Q5+5mTPCehZnIcyt5eM/60CPGVmJ3udEufcOUzwJTfG/J4G1o/9OcMLZqtWUpzH\nzbwdpZ3/i7TQkbUAUEXU+BDgz+5+tJmdijqTgQR9NxQV+486snJC72FkWO0KnOslMWqqjEc4tEa0\nc4q/YN5yrZ7xM3XPKoWHkpvjLajcak8zu6ioQyNnxKyMhPsPXKCSS5FhizUjI2NwmbnF/J41s68h\nIOx90b3PRPd7G9GutsEcE5NfFWUpPOwqyxhvwpXZm8Dqcfdrys4xhPqRyOmWuvSs6e4XhOK0DwLn\nLCUk5ze5+wQz+zmBg+UCZU3GwTjkICpMcd8HIMXlFWRUv4vWuXtc9jawSgEnUXpvAPCiq5yrB2qG\ncIa7v2oCND6SrMS1HqV9/R4ZXsCLSNn8m6mb0g7x3mfwH82WmxOvJmSIt0tZcAGpH4OcS/ujPd8c\n489CmVW/iWuLlnWvgtYydYlajqwzUXJsNHTU5pzhqbzxHZRSP1dJCwWzJZ7NuPieItH3TVAnwBdz\n/5sEfGpmP0OO5eMpgN2R42vbISyj5FidYWbj0bP9CBhjZu8AT3udZiVp/7swaKZ4ASyNguelP4FL\ngdZgmgVeCTKmjkB8vW4mWswv37XpIndPBnlytCVHTD+UQZieST0nUdIBTjKznZEMTBle49BzHEV0\nn/QGGV7x/Mzd9zOzg2Jug8kydJYk+C+tHTpV5xhBqsNMnaPGomd2fPCaR1Awo5msc2WZ6LijvVa4\nDKrWecz9/xpkIN9qZuehjOUWFDw5NuaasttKld+4AP73Rw7KHdD6/hzxjQvc/ewy41WjMK57IBk+\nHWjyOtlvYXCnSFoL2fOsG6wrystd5cbvoUzSRRCe6fdRwOQUM3vO3TcoOFaLKeNwOnpuW6I1mIZk\n1tNUOBXqUe7s7IWgKJpRNv6rZLheqcPkyZbDaKomb+Pv2fH7J2b2I+RY/APquDYK2NnrAF9XmyY6\nWylIdkrMpwdyMqZmIMMoKdfc/RXgoAjGLIqe66slZFZ+rPtNoOGJh+yNMlC3RWf8FOLc1Dvfuee6\nNLrH1ExqFXROZsQ+APHL0mQq1z8dne1XkUNweZS5uGUEY4rIwxQUSYGR1P0ZtG7jke2Ql3dFqa+Z\n7YjgImYjzK7H3P3NEmOkeXiM0wNl2K6PzkrqXL0LckjdG/fQiQK6RlC6p57A3cmJBRlPNWWy7o+C\nnl8o5c7onkg/SXrQRcgmfhBlsU43s9Pq7P2Eh7g7mS4/w7IGY+12QKUxYn16kuFxTo95F4Ji+V+i\nhY6sBYNSRHALZESmlMZlyQyTsSh6WoqJmLBQZnrbMVmqjZkUh+8TqdWmLJsXyBTuMShNutCBNbMt\nUQS1G1JoxqB7noQE3ytesLzSlQnQpra2deaXgJ5/iZwSMxGg32u0Llucirq71cK5WhQJxvRclkPG\nSvp7EG3rBpicWbujiM9QtK+Gh6JThFKGyFZIOPwZwMy+hQRGwnba0NQ9b1RJwf5D9OxOc/fTzOx3\nZFH9JqTkbIrK2UrX97eVTOWvQ4CR6Zx4BTByzGUScpIWHTcpc6sgo+c4ZKithZTuRVEmyJIUT2NO\nz2QEsLEJpH03tHaPxnsJq6ZhJmNOQbgUWDWcIxciXvQndJ7XQ87QMor7l4JcUfffoEh96ujaGfGb\nN9ow5FiybMUmVLKXQKUXR2uejPUimElX5/9OSnZy9uSumw78v2TQ1hgrn63SPTdewgZrRsZqTwoC\nGOfGvBc9w62RY2Msig5vhLIEWhBvGRUGxl31nLahBB5rZje6+w2WAzGOPb8yWqNGAYk0fso+AmXU\nboWcOP9A53FJCjgBw9HfAT2vOcCzZnaDu1+ASkZqOmIaOA3Sc/wcZd9Blu3VG93vVMTTdwAeacQj\n0/eF3Bobr5qOugb8dhDqZDadzPkOKmf+lExej0bybDiSGQ0pF0D5JjLUxpAp/uk1Exm/RbF0/mHC\nyToLldzNiDFaEE/7BdFwpS2O+TBEf2Iq010i5vhye2WWCQ/wIMRziXEnAxPNLOkJ53pF1nI8P4sx\nzkR6YuoeOQWtyQSkW00EPvfiQbgeCFJhGNl+7IbO8wTkJC0bGGw2lR8/gZxDwxDffRvhmpZZk/TM\nR6EMizko424dBInQC52dBMo/09RU4jCv6NYdgY1lUEbdxPjcOyi4dkCM/wsviM2Xv+X4uUZ8b8PM\nsGpkKktc3N3fCT60CCqdnO1q5NOwmU/DiYrH9jFBDLyDAo+9gPdDLypD3dEzHExWwj4ZYbl2RI6u\nsmOmDOBjUeDzFJQBPCuez7ZInzkZ6V2NaA7iCbuhbPs8sHlHlOmWoCUKZW+asH4PQzxzGsqsNXQu\ntzaz09392SJjVXzvdmhfz0aB3++j/Z8aTDyHmgY1oaZB53nrgFVVyt3zVcAmZjbUW3fvBGFAjeY/\npAeaMrsHogSKltADvgts7crsPRg42N1PqTNMus/n0DlPzZPmG/yOCTrmN2jvT0F6xyz03JoRzzun\nQND6f4YWOrIWDEqbuTva6OnvxcnaMHdFzKqQwpBzvFwBPGBmV+SVnfi9I6qlf6OAQZCnxAx6oWhI\nMzJgfoyiBamEr6OZHejuV9ZTuoMBpXa/zTFuDzJMh45x7w0N83DcXYIEx/j4zBhaA51OKsu4ctf/\nCz3TbsgJsyNZyn4HtE5bIEMjz6jSzyQQf4qyZnoiB8osM1sTOT3yQKX17jV11/k2yqDYOiJQo8m1\nfDdh7HR09+E1hpp7afxcDSmFaa67IeVjV8T8L0YKYlkAx01RJtYl8XcHMoDkzxC/S0ZS2XKP9tDm\nKDKTIoRTYz7JkfopWZnPNOSoLAKInaJoK6H7ugIpWVOQwtRsZq+gcq6eNUfJUW4/nYUcTXchp+UF\nqFwVlMU5kRIgr+EEfQlFpf5lZvshpWnpGPvsoobPl41iHT5GvObZtkSyc3QVig7+JjJhBgCfhYJ9\nCjKQqmHcFJ1ro+5LjYBYOyDw6BPM7BV3f5Ssu+swlKb/lhfsKpTj69sifr032pOzEN9eBjlwp6FS\nlTOAM81shKt7ayUlZ/rGiOen7Mz8miwR41wPXFxPtuT+/ydULt0LnbeEj/RPhP/xAZJlDcmzkhNQ\nNsWkkKVOVlaYyIoEYHJOp0vM7DKykspeaA+tjOTFP8gcMIWdJmEELBVzTBmHcxA4esMgl7t/bGrU\nkTqCpWYuS5I17Fgift8YyY6LrBggdFrz45GhPx0Z6OMQrxyHePA4M3sBuL2I/HZ1MUy4cmuQYeI9\n7SWyaeLsrov0iKno+c1B+G8pUwkz69Ae4yT21FnIOH0ArVECzk/Ooy7ufka1z+e+9xtorZdAjq8x\n6N495joJeM3MfuvuNUtXc+dqU4Tn8zFah5FI1tyLyuTepQEOazhF+hFr6mqQMBvpU/+q99lGlLvv\nu1AwJ+EnLkLrsuR0nnoiuVYNo3PNeM0mw+MzpP+MjM9dY2bT4v2DPOtSWmSOt6EgYZscWSjL8Fdo\nfb8V470aTs4xyBE+kswRN7ygrgKAmX0VybDVyModR8fYk00A63tWOlIrKXe/ryL8wQtM0ApLIhiF\nOWa2NVmQrCwthtbzMs9lXYXc+rOpzO4g4OBGZ9LdPzCzq4Hz4qzfgvZpfxR83ZQMC62RfE38bjck\n985D+lPahyshPneJqbvghzUHaz3H5HD5SThbe6HgwuLonPePv5dEzp4eSGZc2WhsE+bdOKTzj0Ky\ncVA8kzfRPt8Ydf48hlz30vj8IGQLjUf7pYksO3c2Wcf49jpt+sX4KfCxE8pSfyD0ms+IapZaa56T\nmX9HdtpPzexvZHiWs4rI60rK2WJfQ7rJQ4gPfRd1al8LdcztiKoyoA60xP8aLXRkLRiUDtc9yNjZ\nESneg8iyLDZH+6FQSmpO0fsaKhFLinL+cPdARs12wONFlbDcNX9E3QoTbk1XsjaoPRFTfSx9rM6Q\nJyMQvF+hyEjHGKc3YtCFHXhx/epI4UjgsWk8kGL3LBnoZ1n6A1qbDrTG7EmGx6Jkqddz7zn3/J81\nta0+MhfpdDP7DkqnN6K9cSPKMeXl43uTQdopu8TnoKjUmsAWDQyLNN++KGI7LRyDGwEXuwASU3nL\nIkXmWEHdkbBL37MMmaO2E+XWeX7S88gJ2xmt4xbImFgUKdlLI2yARH8A9sk5i2tRMnYHoOfZYgKK\nnYD26ShkELyLBN4NFBdur6N13QgZJtciQQxSRB6lgOM3T57LdnD3v9MYX+tLT2a2C4rsLhP/+hnq\nmLUF2ufPNlLa8+TuL5vZT1AL+XeQonkASvtfDEUMh8e1/4kSzRZTY41VgYdNLdVHIIVzKaQgH1Ji\nyISt8X10du8Mp3wCXx1uZsnpdAsqTXgQ8ehqjqx0ZlIJzwcx7/yzegudoQEVn6lHT8f3TXU1F/k5\nyoZcBzmHjq4Sga5Jufn8FdjK3f8Qf7c7izT4+rR4jUbOh+dMXYITtk4hisyFq1GgaSyShVORk3sk\n0NnM9im4x2e6gHGHIbl1i9fACArDolBJbu6aY5A+8iYygLuhc7kayqCbiLIN3jOzbQsEZnCV4s7j\nLCnpcPoaKglqQrIrgbnPDIfGWGTcfYJKVt9x95qNTOrQQISxt6+7z6MDmEr6+s7zqXnpTBQs2hOt\ncx9kaG6BDNKnUWnN/ma2gau7ZjVKz6cJOSNakLP3RpQ5NaGE0bcL0m1HkD23cUgfmBY/P0P7vQl4\n2wtio86drNYzYfTVpZwjupL2IsMTG0BWJjwI6aODyPDwhlK+ectxwKlmdgTiO2PJznoTKiGtty8f\nINOZPkYZkZ3Juj6vHXNPDudCnVhhrrP7JiT3DkV8ZgDKVF4Cya+vlJSHnwX/vwhBovQHOpm6YG6D\nqjguKzpejlpQVs3aKDhaSZ3IcOpqlkzn6FKybqw7k2VvTgBOcfcb4n6KNs/5JsKsPNNbY9O+YGYf\nIOfdNsBvCzr755KrND8FW4tWW1SfrNZ8BNrba5LhHq+ByrOTfE9Nb45w98pufrsh/KwJyNE0kSyJ\nIDlBR5vZGLSnXq/nQK9DKZvpQjO7ETkqUyftLshJWHfNc3z/QuTo2wDpJG/FPMeY2di4jz94Hbyt\nyqHj5+boDB9qApN/yd1/Gd99JuIfZ0ApuIovPS10ZC0AlBNctyKD9tcmzKCvAAMjY+ksJAwagmqa\nWlpviKIz3YHFzWwDxPxSne40dPBbiMNfxrAKhlBXacgri7XGjkhdD+A8z7CIZnnWzrxQxCJH7yPH\nXBLkfZDwXAXVzjtZ++rSFIpbmzpI5ugS5HzYk0wpugk5/Y50pYjXJTNLDruPUcrvRIJf5J0rZtYN\nGT8pAl3P6EtC+hVgexMI787xmQQgmRS6toDZPo6iUeug8qO+ZGVX2yKFLCklX0hZIYCrTOBGAFN5\ny05IobkOCchuyLA+FGUInBUfbSSI0p6fDkwwlY18jJwF3ze1Gf8aahN/f8k5O8rcmyd7z923KZsZ\nEIr9GjGfzohHpA5sU4DPvBwmyH8tpWdjZoegks7bkAPrXrLMuI2QUrIb5dqXg3ADX0XG91BkAExA\n2HIvt/8O2kfuPs7MtkOR5o2RkdIRnb273b0ym7TucPGzCzJ40nfkP9tE5iR7L66tZQzljeeU+TMz\nMp6S8tcXOZfvLDC/NJ8Wsm5cuPu9aL3bTCGrZqIS37+hYMwIWsvZJm/QRbME3Yfk/zEF59cJyZpV\nkLL+J9Sdqz/SM76NmgYUNUwTvsjmSC7cHN9TDWeoLfx7v5jjuR4d4mL8pZEj9Ez0fO9CgN77ettx\nOcs4kf+OgM57xWs3dP/pLC+JmngkJ9ONwI8LBDoqaQAy/J6DuUZmapzTEvdaFzIhsiN+Bfzc3W+u\neO96FIS5AsmNp1EG5q6V40CrZ/QkkokbIt3qciTHnjez+1CGW6P1/oDo0ob0nRURf5yNdLYuqLww\nNb05GgUUSkEMmEqSf4pgOeag/fIkAv+eq0vWWv8wXGcgJ3qj7yq1viFjN0CZLX3RfSf5mkppP0Br\nUpXiXKS5vYWek6Gz2YnWQeWyWLCLob28ubs/1+jiEvQwChrsimyaAShr8Ergci+Y/VtB45Ej8ChT\nGd8T6Dl2Q06kDZFzqhC5srIvQyDsyyFZlgJZI0rMK+3VCfH5Zph7lju4stpfQeuSx+EqRCG3BwJ/\ncnW37YRKDAej8/xwzMGL8Li47z3R/kl7pwdZUkKyn/oih241PLM/I92hN5nTM2WKfSU+04csUHwy\nJToC5uY6IZxBx6FMuReRA434ro1pYNvlnsmpcT9LoESAIcix1R/pgF2QI68opTVcnqxBywqoJLyX\nq3HOhUhH2A24qixv+1KTuy98LUAvpChdgITcJ0iojkWRyF4FxzgcMdIpSFFIDqE3UDbS/UgJ/RfK\nRuleYn53AxvH7ycgx8Q+SDH+GlJQBqIU+CLjdUcK6pE13jckAObHs90YKcHrlPiMIafNV+PvO1Gq\n6Okoc+F7cd/LolauZeazaHx2U4R7UOaz66PU+JZY41lICRyBGPz9CCT6rvjfnvG5jgXG7oOcanNi\n/H2BrvHe8cj5tFwb1+B+ZOhdHj+vj739KTKyev47zlWBeXWOnynjbp6zFmv1GFL0QCnORcZeCmXl\nrBh//xFFo+9HTpJXgY3ivYZ7Pc7Xjcihdgwq59oGOQiHIUWx4TrnxuuPykVbkCH1VvCGd1AE/l3g\nqqLz+29/pXWL+zsjnVvEZ3eJ39dBCslS/47vbsPnOqLoaJs+X2W8RRHfPg8pWD/MnYHCeyc33i5I\nVl0XvCmVmq2CoqafIh75FRRJXr/BeJvGelxMjq8i5frC2J8btOeZzodnuDi5Lr3xao6z/S7iwzeV\nGK9DPMfNUMbTysE7BiMn87XAJyXGS7g0W8Tvs5DC3in4xf2o2Ubh+cXPg1BW3dD4u1Psz7RHS/MI\nJGebgE3i74THmPbkdxHulsX3jwI6fYFrne79m8gYOQIFdVKgYz1kBF8LDGjjd3RButspjeZR7fnF\nz7WQ3rd8/J1K7NJzPBBlO4H0t3faMM9lkdx5hsxY36rE59dGeuiJscfXQYDvRyNH90Nk+lbhs40C\ng+NROfLVSE97NM7jvZTg5WQlhSnzvmPu91L8Jrc2qyI9/GHkWD4R8be/xHl6C2W0fiF7uso8l0H6\nzWbxd8cqr3bJ//Qs2/H5jvFzf1rz3Y+QY/k9sszBg5CM2wVYqd6c/g3PcnXksNqjynu/QPZX4f2Y\n++yLwFG5v49Hev+7SM+vK1fnw9q16VkhHrkoSp4Y2M55DER6Rafc/1aO87R6/N0unQBYpI378neo\n3BUURHoc6Bt/D0V2/R7/rn333/pamJG1gFBEFQ4Bbnb3w83sSGSUzkSKv3vs/kbk6gL3O6QEv4Ii\ndM1knvLFUfTledTprHCpAsrkSt2stkVOj9Qhw5AQmYU6QQB83XMdMKpQH3SfPzCzT1D5xzgEFD8j\n7rlM9LQePRdz25Hq+AjVqCtyPHwW2U0DEEPeEHnuuyIFtAMKuo1396XyA0R2XTN6bjPIwDkTAG9b\n6FWyLmt/QQrc7YjJD0LruxJSYq9EJaB4sVKPScA3zWw59Ow/9CzyOAdFdNuanfN9FDHdOua8Diod\n+QtwohdP5Z3flCIji6F1mglzzyWuTLy3UMRyibi2SMo6rkjwFSmrBAnc11CU/2HgUo/25V4sQtMf\nGbbLUL18thkZGXXLZ3MRoc2QsncscpAtTlZaMQCdz7dqjfNloxwfHYwyUlJEuDdZOaaj51C6DbOZ\nrYCySxL/fg1lLTxVlIfHOMOQs+YvlefWWncsLDO3TshYPhM5kpNz4DuoXPZEdy/dFdfd/xJlbL9E\nTpLPkGOiH1JiT3T390KufUCdzrSR4fOomZ2LjO1vm9nraC1WQnv/BKTUU/YZzC9yZUd0MLOu6Bwm\nnJJlUFR/FaIMsGD0dRAZWHVy7ExFMrcbkjG/ajSvXKnKYMQT/on24jTk0JgN3GNm3wB+jcCHG84v\n9/4fUWbJz83sJJ8/jWR6o2e1KfC4z5slNhBYzN3dzD5CgYb5BtBbgFKJza5obS539+mRZTPL3Z+L\nUpLL4h7+WjSrMffs10WOpqGmroVPITn7OdKJJhfYQwnXaG/gl57LWDPhw22O5C5IZjTEPTThJq2F\nnKAJ66oPWWOepAPWG8PQ3mtGwYMngPO9dQfT+81sNipxGgOlz/YpKHB3HDDKVeLcC63H71Cnt92L\nyNjc984P3pL0hNWQrri/l8CtmmewyAQzs2tQt+J3yLCxPkXOsk8R7/igiM4Hc7GifouabHzi7m3C\nLjOzA4En3P0NM9sQ6SdTc6+ZZjYHycbmovOLOaZrb0aB+W7IQbAkktf9UWnyUohXdkbJAedQu3P3\n4cELj3H3t81sCSTHvoHsp9O8QBlz3Hs6y79GeuJvTWXsr6Nz2Rs5bX8HDAn9coqrQVWjsfsiufJ0\n/D0QOcUuQiXuKbC5Y5G5lqUi56aWHPEs07Hd0CGukuMxFf97i1yWVzW+kcvEH4aCaz9A5yadT0f8\ncwDaTw1B8nOU7vlJYCszWwxlqt2G8OFuRk7VLmSQCv8RveU/QQsdWQsO9UO4Ki+Y2ecIX+T9tg4W\nCsIbZraeF+haUYJ2Jyut2wUx6wFkgJqpnK8P0LuWEyun5K2MDOkmlAkzEilHs8xsMlK27nT304tM\nztTJInVKmUkGajsLRQJXpgT2j7vPMLNfJsZoZpshgdmbDDy0N1mnuMr7A0Uf+5F1EBpvZuNQ9svn\nKLo8Mn6fSEXnlFrzIvDSTMDck1wYVqmTzRxgdkHHSK3veLfKv89pjwHhwqc5F5VSLooUkY9rfNcX\nSek53YeUkCPM7KLk5A0j/WdoHdNc6woiywAgX0NC7WwAV6r6Oe2Y6/wqn82D+/8LOdSmUMfJ0J79\n9N9EoUC+g0oI7wxnRCeyznWrIwWzcNlDlA8cgNLdP43xe6MMpb1j31/TSHHPKYProXb3FyHl9U7U\nuruwcZIbsxoI7UWIV/ZFmbTHIfD0wiC0FXQtCo6shZ5fN2SA3+RZB8ir0DOoqdDm+OaFiHfuhPb2\nEGSE7+eBWfJfQj1QmdRiSHa94DmMo5AFRc7N58go64N4Y8Lm6YfO9PPu/kijQXJ7oydyhKRSo6mo\nJHNE7vKBaZqNxs2VUt2A+EwLsK0JZy2BgH+M5Nz97l7GCTwN7ccTI2D0GHoes1GJxklk5dcroGz1\nL5LSnkylJ7NhHoNpNJluAAUDHblrFkc6y/Mo03sfdIYsvq+TmZ3j7kdbBa5Omoe7v2JmFwK/MsFJ\nPI6eY2dkQG+AAI47oedYs4tljg+dj/ZlE1rziSjz5Spk7L1JFtysfoMyIJPesBxyVk4Nh3zK9GlC\ne+tY1PVrVAlnYMKvusxzJfAhz+4wsxNQw5L/hPxK85+GHCOpu2QyrOc2YCo0WKZ/vYL2R3fEHzdC\n+zNf/rURCmoVpUnIiH85AstjkX76CQK8f8CFn1mPfoWClW8gB8sShCOWefGTmszsjLLOcBf0yFP1\nrok93h3xz3q4ad9DDogEl3E0sm3uRsH6aWZ2pBcI+Of21zPo/noj+bpUzGMAepZbIeD+2UAPM1vd\nG5egD0R6fXJ6bYnOzQlxv39GztyyGIDzhZKcC76zJQqozkRrn5onTQfebW8Qoi33l7t+caSTTfQq\nGH9mtiNyCH6l8r0CY/8ZZZROQHvgLBTU3B7d+wkEz/2i1+c/SQsdWQsOdUQ13/3mZ6QxObFM+Dyd\nkUOniSwaUhi4McZLmSo9gP/P3nmHyVlVf/xz0nsllRZBkG5BAipVRHoHBX5IEZAqUqRIF6SJ1KAi\nIII0KQIivfcmvZeQhBLSK0k2/fz++N6b993N7Mz7zs5mNps5z7PPZjMzd+77vveee873BaYGFAAA\nIABJREFUnPM9Q939zjLnFTfxe8j5a0NShx2d867oACiZpZMysq9ERvZIdChPRIp0AYpGTiEhoM81\n15BZtb2r1XqmzwTZlSSTawBJxtQgBCJELpiY3dWZfCSiI4HOKYepLkSVVjSzkZ6TNLWYlLs2AxD0\nCALC7kZg0PDU67kILystKUfgCjNbGR04h5jZJ2gdDUKR4r8QuptlOIji6+0J3RmD0dou9bohwDHz\noRYO34LAn5ndhw7hlwu93kCi0TUSGUd9gK8t6cC2aNhqPpvmEFe0/i/AtWYWOwlG3bY1uof/zDns\n91G23VWom1fMlFsJiATwY1BTj2JzWxj28p2mjqM7oA5VpwC/M7NnUanMO8Aoz8YTFIGKLShMQvs/\nazoJ7YIwp0a7/+UBBgNY/5SZPY8ycCrFNVURsfrZbXVoH7UFepnZR8CZ7v5sDgfVCR3HQkT3szDu\nIoPbzDoUO7NDlL6zq5PaZAQ4bILW3HDgajP7A8ps2wdlwmaSlO6/HgGMkfB6AOJp3BQ5jpGv5IWs\nDocrw+RS5Nj9AjmQc9GZOAQFnw4P1/d91DhgSUrUlf9GGcmXmDgOx8CibpXHoHM+VyZL6v48gECs\njmgddUR2QS/kEK9EAko0ek/d/Vwzex9lZe2M9FAHdAYd7+43m1m6zK2xceI1x6DLKODTcs+C1Hgv\noWd5bwC40+Ptj555Xt7WyJO0QRi/0OvjoLQTHM7ohsButA9yO5+pz7yKgN4TzOxiYJyZxQ6YCyny\nTBuRK1G2fXt0zzqS8E8djM6vNxr9dJCo583s54hXaiQike+IbNZ+CNxaC92XZ6wIP5i794vgHLAZ\nAgSGhDEih9KaCGRbEQUOc0kIWEMSsJ7b8NmE+U0PP8VkNXRmfx0CXPuhSpU/mdme6Nw+jxxNNtz9\nD6m5Rv6paNv3QqBWzOAdjGzMUtIWgYo/NLPPUGOCx8J3dCQJpECZHfGaAoAFUPbHqFx2JtLTM8Jc\nuqTeujLlV3Qs+q4877ckM7NTmNd4YANT86rZ6NnOQOt7ZZKAZt55zaV+kOViM7sTgWcf5rF/WpNY\nmWuqJkuZhIjSqUjBH0vSzWVeU5BbUze8Y9GBtIAE3BmPjOSX8oBRqUNvA0TeuRH10zMXhtc3BnZw\n90zEtI18lyEF6FmiIeEzm6PSk15IsQ9Ah3sPVB51jjfepaexMWMq94HImRxKoqAjGesCMzscWNvV\nIrdgim24pvaEbKlwcMYOjd2Brp6TaNOUtjoVONqVQfZjZBz0QkDhsV5ehkXFxEROfzcyXr5AXDo3\neipluxpRpEISsnM2RFGzdRDwNB0RYecFNzCzExGv3E4VnWjh74qdh95z99MyfmYQMoonoGyizwpF\nqlqbhL33OwRaOTIyRyJd9hbwywZgT6nxfoW4czb3FFl1eK0LchpHuvs+eUGiAAQPQk7KYeicmAAc\n5e73Z3DOYnbg5UinHuPqSrqIhDZ8x/+Ay939qmLOSiPf8S20vzsi/TgVOc9TgImeoXwizhUBGe0C\n6B3/72dh7i+6SgmqIqkz8ACUOXI1yh7rggIwa6CzfBaQObstOCOHITC0C3LQZpGc2XOBW9z97409\n76BrVnR1TVoXnX2vu/t0U/nMqcip7ICydc529xF5dW9cNyQATw+SUvtewJuuDJtcEsDBjVBG3/Lh\n+p9z96dT7+mKbOOS3ekqLSnw8igEug0Pc1wZOfoXoGBNHrqGxr6rfVoP53lGwc4YiIDGXsiWfM1F\nEN0kkuEY6Ch3jBBUfRzps5dRRtd89Lx3Q2fRCXnGD/r1NwiEGIaayUxF63EDlN12n7tf2dTrzysp\n+/Fy1DAGBCJ+iOzw8QigmI1I6UdW4DtPRuvxkFKBjtT8/oGewYEISOyMwJMOSKd3AyZ7kWzaBuN2\nRc/jjqyfyTBm1L3/Q7QU41E2YKxw+BwBrgZc5+6TMow5EjgtALzbILB6ZXefaGbroWzo5asNQoQz\n8GQUJKtDa+hAd3/ZzFZF636iu++X177I+P0l9Y+ZPYnW8nEoiLIX8g8PQokEp6IM7ab4tJGnLo/9\ntBo6o7dC965DmOd0tH6mojO2D1pX17r7GWXMbS20j2eSAKyxqqNF+DfVkFpGVisXq19Gcix65puh\niPuXqAxtAtocH3jg0skyZjBcL0Mb935k0AxCRtd3EGnnrYjPIZPT4vXLFtp54y3Lf4QyCU4upVSD\nYlr0FeF7PGz6Yvxaheb3FKmMq0oYLan70hMYV8S53YCkvDBt5KfHcmCumXUNAMICdPjk7YyWlo1R\nW9yYuXYRUqLXohTv35jZ8dVUoq5sis1MnRAPRQfc/mb2AOrW+XwljP9KSHDAngaebuhM5JXgUPQH\nvmvq8HQvMrRimekMYGZWJz+MWZHy2dS+vB3tVxCH2VemFsTjkQ6ajcoOx2SdY0uX8Fzmu/vZZnYb\nuv4e6D5+4u4PlTFsHXIAuobvSPOrLQivRec+U6eiAC6thwDVISiCPRs9mxWRswElIrApHfh3BCLv\nAdzg9bN7Dkf8DQ+Fz2QCsUK08yTU6TM6FXE+c5HR+Ara71n08SBUInFlGL8bIrU9Oow/3Mz2qOJ6\nLJXd9pqZjSJjdlvqtb1RCd2DiAOmM0nmbj+S7F1IOgg2lA2Q8w7KKnWka0HlM4choK0OlSrOLMfA\ndnW76oj4wPqicspK8J/Mp5FurKn35LIJKikpQOINBCqvg2yh91C201PljBufgZmtiYjtVwc6mNlo\nVFL5bJb9aGadPeEWHRN+Gl5DLnso2Gdpu6xJDrK7jzZxJ/0K3cNN0J6ajtb/RVnnmNInW6IGPCC9\nMRrpoc4o42YU4GZ2MDDNzD5297cajLUnKmMeGeYypcHPtPAzA5iVdc+kntutaJ10R+WVK6DnHPlW\nB6Ln1WQgC+nbU9x9vwzvjfc6XtuksBbr8uqFBtILBVRfMLOpJHozzT+Wi+cxtfZ+i86/Pui+9Q9/\n741s8IVIhxYFssLafhxlyS1AOvOxlC5bHdlnVc+kCT7dBQikXRWd1ZFvaQACY2KGbe7nZmYx+25k\noSBEBhCrKzp/NkegUEdghLu/C7xsZjcgzs9/5Z1bg3nk9ufc/RMz2w+ti78hG/k6kvO1PwJ+2yD9\n8de83xHu37sIwJuL7LSpJqqgL5Bd/QXS5RWrklkapAZktXJJbcqJiHNgIVJS6yBAK7YtbYs6qh2U\nYdh4YGyFgKCfuftHJeaRxUgaQpI11gkR3Z6HoiCx5v1L5LisjyJO6fk09t0Vi46ZMn/OQjwtjoyW\nSKo+Fhkk/8zhpG2IuDtWR8rJTbXonyOjYzQyPFZEIEJsMV1Q6Qdldyoy3rojg2mUmd0O/Mdzlnqa\nWSRfjXxZ64drX8fd3zdxcZ3m7sflGbe5xN3fNbPfIT6dHVAL+HVQadM/3L3RsqQlKabSiw6Iw2E2\nchrnIKMmjyPVF6X5L0D7MWbzpaM2n6Nsk1Jzqmj5bMooPAat354ozX0wCUfPasjxvQU1PFiqo0qp\n+a8J/MLMnnT3R4CPTCWFuwM9zWxMQ0cng/wb6ezfm3j1vgjf2QlFuWejdV9U36Yi5P9F++M1xLc1\nk6RcbAQian8rjJeVd+s8EhLa45BjVYd00TYI/B4UMk+KktCmxtwI8UBcgUCy5ZBOir9XCvMtKqln\nM4QkWwNUtnYYInN9DfF7HQOcVKX1GM+rqVSmxXq6DOcZlGVXNBOwyPoZAWxt4ikZCLwb7094ViNp\n4CjnvX/BYbkElcy3R+fqbmb2ISpXfMPd388zZhh3JWAX5ODPpD6B9STUsXFJErwXlABMP06Rsrwy\nxnQz+y4CmldCZ0IbxB96IiJC39cbyTBJgaGPmNmP0L2bjuyysWhdjEDBjrtKra8Gc6t49lL4/gtN\n5aR9EPg/upADnXFuT6GAXicS8u/I3/px+L+z0JneHZ1n+zYYbhXkiH8rjBMDrLGB0VykK6c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xjWRhkCayEjb3RWJzzo6ELdjZ5BpTijw/uqzvkHTc9uC2NUIiOiWST1zJ5FmRbXmZpgfBuYGNbr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26045903e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def random_color_func(word=None, font_size=None, position=None,\n",
    "                      orientation=None, font_path=None, random_state=None):\n",
    "    h = int(360.0 * tone / 255.0)\n",
    "    s = int(100.0 * 255.0 / 255.0)\n",
    "    l = int(100.0 * float(random_state.randint(70, 120)) / 255.0)\n",
    "    return \"hsl({}, {}%, {}%)\".format(h, s, l)\n",
    "#_____________________________________________\n",
    "# UPPER PANEL: WORDCLOUD\n",
    "fig = plt.figure(1, figsize=(18,13))\n",
    "ax1 = fig.add_subplot(2,1,1)\n",
    "#_______________________________________________________\n",
    "# I define the dictionary used to produce the wordcloud\n",
    "words = dict()\n",
    "trunc_occurences = keyword_occurences[0:50]\n",
    "for s in trunc_occurences:\n",
    "    words[s[0]] = s[1]\n",
    "tone = 55.0 # define the color of the words\n",
    "#________________________________________________________\n",
    "wordcloud = WordCloud(width=1000,height=300, background_color='black', \n",
    "                      max_words=1628,relative_scaling=1,\n",
    "                      color_func = random_color_func,\n",
    "                      normalize_plurals=False)\n",
    "wordcloud.generate_from_frequencies(words)\n",
    "ax1.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "ax1.axis('off')\n",
    "#_____________________________________________\n",
    "# LOWER PANEL: HISTOGRAMS\n",
    "ax2 = fig.add_subplot(2,1,2)\n",
    "y_axis = [i[1] for i in trunc_occurences]\n",
    "x_axis = [k for k,i in enumerate(trunc_occurences)]\n",
    "x_label = [i[0] for i in trunc_occurences]\n",
    "plt.xticks(rotation=85, fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.xticks(x_axis, x_label)\n",
    "plt.ylabel(\"Nb. of occurences\", fontsize = 18, labelpad = 10)\n",
    "ax2.bar(x_axis, y_axis, align = 'center', color='g')\n",
    "#_______________________\n",
    "plt.title(\"Keywords popularity\",bbox={'facecolor':'k', 'pad':5},color='w',fontsize = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据缺失情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_name</th>\n",
       "      <th>missing_count</th>\n",
       "      <th>filling_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>homepage</td>\n",
       "      <td>3091</td>\n",
       "      <td>35.644389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tagline</td>\n",
       "      <td>844</td>\n",
       "      <td>82.427649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>country</td>\n",
       "      <td>174</td>\n",
       "      <td>96.377264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>actor_3_name</td>\n",
       "      <td>93</td>\n",
       "      <td>98.063710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>language</td>\n",
       "      <td>86</td>\n",
       "      <td>98.209452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>actor_2_name</td>\n",
       "      <td>63</td>\n",
       "      <td>98.688320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>actor_1_name</td>\n",
       "      <td>53</td>\n",
       "      <td>98.896523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>director_name</td>\n",
       "      <td>30</td>\n",
       "      <td>99.375390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>overview</td>\n",
       "      <td>3</td>\n",
       "      <td>99.937539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>duration</td>\n",
       "      <td>2</td>\n",
       "      <td>99.958359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>title_year</td>\n",
       "      <td>1</td>\n",
       "      <td>99.979180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>release_date</td>\n",
       "      <td>1</td>\n",
       "      <td>99.979180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>num_voted_users</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>vote_average</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>movie_title</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>budget</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>spoken_languages</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>production_countries</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>production_companies</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>popularity</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>original_title</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>plot_keywords</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>id</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>genres</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>status</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>gross</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             column_name  missing_count  filling_factor\n",
       "0               homepage           3091       35.644389\n",
       "1                tagline            844       82.427649\n",
       "2                country            174       96.377264\n",
       "3           actor_3_name             93       98.063710\n",
       "4               language             86       98.209452\n",
       "5           actor_2_name             63       98.688320\n",
       "6           actor_1_name             53       98.896523\n",
       "7          director_name             30       99.375390\n",
       "8               overview              3       99.937539\n",
       "9               duration              2       99.958359\n",
       "10            title_year              1       99.979180\n",
       "11          release_date              1       99.979180\n",
       "12       num_voted_users              0      100.000000\n",
       "13          vote_average              0      100.000000\n",
       "14           movie_title              0      100.000000\n",
       "15                budget              0      100.000000\n",
       "16      spoken_languages              0      100.000000\n",
       "17  production_countries              0      100.000000\n",
       "18  production_companies              0      100.000000\n",
       "19            popularity              0      100.000000\n",
       "20        original_title              0      100.000000\n",
       "21         plot_keywords              0      100.000000\n",
       "22                    id              0      100.000000\n",
       "23                genres              0      100.000000\n",
       "24                status              0      100.000000\n",
       "25                 gross              0      100.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df = df_initial.isnull().sum(axis=0).reset_index()\n",
    "missing_df.columns = ['column_name', 'missing_count']\n",
    "missing_df['filling_factor'] = (df_initial.shape[0] \n",
    "                                - missing_df['missing_count']) / df_initial.shape[0] * 100\n",
    "missing_df.sort_values('filling_factor').reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来，homepage和tagline缺失比较多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "电影年份（时代）统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_initial['decade'] = df_initial['title_year'].apply(lambda x:((x-1900)//10)*10)\n",
    "#__________________________________________________________________\n",
    "# function that extract statistical parameters from a grouby objet:\n",
    "def get_stats(gr):\n",
    "    return {'min':gr.min(),'max':gr.max(),'count': gr.count(),'mean':gr.mean()}\n",
    "#______________________________________________________________\n",
    "# Creation of a dataframe with statitical infos on each decade:\n",
    "test = df_initial['title_year'].groupby(df_initial['decade']).apply(get_stats).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8xhi79rhxChhjP3T4CRBCui1a8EYIIeS0hRPWSQD8AA4AGABAB+AzzvkUxlgMgF0AegGw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\nzfvqfug6tnv76pe2o6MpgQ/cfj4CQf420R8RtMug22fB1h9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MYHv+yuyRGIZhR6PRe++8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\nK98Q4vcVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x260308ef080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"poster\", font_scale=0.85)\n",
    "#_______________________________\n",
    "# funtion used to set the labels\n",
    "def label(s):\n",
    "    val = (1900 + s, s)[s < 100]\n",
    "    chaine = '' if s < 50 else \"{}'s\".format(int(val))\n",
    "    return chaine\n",
    "#____________________________________\n",
    "plt.rc('font', weight='bold')\n",
    "f, ax = plt.subplots(figsize=(11, 6))\n",
    "labels = [label(s) for s in  test.index]\n",
    "sizes  = test['count'].values\n",
    "explode = [0.2 if sizes[i] < 100 else 0.01 for i in range(11)]\n",
    "ax.pie(sizes, explode = explode, labels=labels,\n",
    "       autopct = lambda x:'{:1.0f}%'.format(x) if x > 1 else '',\n",
    "       shadow=False, startangle=0)\n",
    "ax.axis('equal')\n",
    "ax.set_title('% of films per decade',\n",
    "             bbox={'facecolor':'k', 'pad':5},color='w', fontsize=16);\n",
    "df_initial.drop('decade', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "电影类型统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genre_labels = set()\n",
    "for s in df_initial['genres'].str.split('|').values:\n",
    "    genre_labels = genre_labels.union(set(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Drama', 2297],\n",
       " ['Comedy', 1722],\n",
       " ['Thriller', 1274],\n",
       " ['Action', 1154],\n",
       " ['Romance', 894]]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_occurences, dum = count_word(df_initial, 'genres', genre_labels)\n",
    "keyword_occurences[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_duplicate_cleaned = df_initial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Collect the keywords\n",
    "#----------------------\n",
    "def keywords_inventory(dataframe, colonne = 'plot_keywords'):\n",
    "    PS = nltk.stem.PorterStemmer()\n",
    "    keywords_roots  = dict()  # collect the words / root\n",
    "    keywords_select = dict()  # association: root <-> keyword\n",
    "    category_keys = []\n",
    "    icount = 0\n",
    "    for s in dataframe[colonne]:\n",
    "        if pd.isnull(s): continue\n",
    "        for t in s.split('|'):\n",
    "            t = t.lower() ; racine = PS.stem(t)\n",
    "            if racine in keywords_roots:                \n",
    "                keywords_roots[racine].add(t)\n",
    "            else:\n",
    "                keywords_roots[racine] = {t}\n",
    "    \n",
    "    for s in keywords_roots.keys():\n",
    "        if len(keywords_roots[s]) > 1:  \n",
    "            min_length = 1000\n",
    "            for k in keywords_roots[s]:\n",
    "                if len(k) < min_length:\n",
    "                    clef = k ; min_length = len(k)            \n",
    "            category_keys.append(clef)\n",
    "            keywords_select[s] = clef\n",
    "        else:\n",
    "            category_keys.append(list(keywords_roots[s])[0])\n",
    "            keywords_select[s] = list(keywords_roots[s])[0]\n",
    "                   \n",
    "    print(\"Nb of keywords in variable '{}': {}\".format(colonne,len(category_keys)))\n",
    "    return category_keys, keywords_roots, keywords_select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb of keywords in variable 'plot_keywords': 9474\n"
     ]
    }
   ],
   "source": [
    "keywords, keywords_roots, keywords_select = keywords_inventory(df_duplicate_cleaned,\n",
    "                                                               colonne = 'plot_keywords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 {'alien', 'alienation'} 2\n",
      "2 {'spy', 'spying'} 2\n",
      "3 {'vigilante', 'vigilantism'} 2\n",
      "4 {'terror', 'terrorism'} 2\n",
      "5 {'flooding', 'flood'} 2\n",
      "6 {'spider', 'spiders'} 2\n",
      "7 {'horse', 'horses'} 2\n",
      "8 {'music', 'musical'} 2\n",
      "9 {'animation', 'animal', 'anime'} 3\n",
      "10 {'compassion', 'compass'} 2\n",
      "11 {'train', 'training'} 2\n",
      "12 {'sailing', 'sail'} 2\n",
      "13 {'time travel', 'time traveler'} 2\n",
      "14 {'orc', 'orcs'} 2\n"
     ]
    }
   ],
   "source": [
    "# Plot of a sample of keywords that appear in close varieties \n",
    "#------------------------------------------------------------\n",
    "icount = 0\n",
    "for s in keywords_roots.keys():\n",
    "    if len(keywords_roots[s]) > 1: \n",
    "        icount += 1\n",
    "        if icount < 15: print(icount, keywords_roots[s], len(keywords_roots[s]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Replacement of the keywords by the main form\n",
    "#----------------------------------------------\n",
    "def remplacement_df_keywords(df, dico_remplacement, roots = False):\n",
    "    df_new = df.copy(deep = True)\n",
    "    for index, row in df_new.iterrows():\n",
    "        chaine = row['plot_keywords']\n",
    "        if pd.isnull(chaine): continue\n",
    "        nouvelle_liste = []\n",
    "        for s in chaine.split('|'): \n",
    "            clef = PS.stem(s) if roots else s\n",
    "            if clef in dico_remplacement.keys():\n",
    "                nouvelle_liste.append(dico_remplacement[clef])\n",
    "            else:\n",
    "                nouvelle_liste.append(s)       \n",
    "        df_new.set_value(index, 'plot_keywords', '|'.join(nouvelle_liste)) \n",
    "    return df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Replacement of the keywords by the main keyword\n",
    "#-------------------------------------------------\n",
    "df_keywords_cleaned = remplacement_df_keywords(df_duplicate_cleaned, keywords_select,\n",
    "                                               roots = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['woman director', 324],\n",
       " ['independent film', 318],\n",
       " ['duringcreditsstinger', 307],\n",
       " ['based on novel', 197],\n",
       " ['murder', 197]]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of the keywords occurences\n",
    "#----------------------------------\n",
    "keywords.remove('')\n",
    "keyword_occurences, keywords_count = count_word(df_keywords_cleaned,'plot_keywords',keywords)\n",
    "keyword_occurences[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get the synomyms of the word 'mot_cle'\n",
    "#--------------------------------------------------------------\n",
    "def get_synonymes(mot_cle):\n",
    "    lemma = set()\n",
    "    for ss in wordnet.synsets(mot_cle):\n",
    "        for w in ss.lemma_names():\n",
    "            #_______________________________\n",
    "            # We just get the 'nouns':\n",
    "            index = ss.name().find('.')+1\n",
    "            if ss.name()[index] == 'n': lemma.add(w.lower().replace('_',' '))\n",
    "    return lemma   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \"noncitizen                    \" in keywords list -> False 0\n",
      " \"unknown                       \" in keywords list -> False 0\n",
      " \"extraterrestrial              \" in keywords list -> True 4\n",
      " \"extraterrestrial being        \" in keywords list -> False 0\n",
      " \"foreigner                     \" in keywords list -> False 0\n",
      " \"alien                         \" in keywords list -> True 80\n",
      " \"outlander                     \" in keywords list -> False 0\n",
      " \"stranger                      \" in keywords list -> True 7\n"
     ]
    }
   ],
   "source": [
    "# Exemple of a list of synonyms given by NLTK\n",
    "#---------------------------------------------------\n",
    "mot_cle = 'alien'\n",
    "lemma = get_synonymes(mot_cle)\n",
    "for s in lemma:\n",
    "    print(' \"{:<30}\" in keywords list -> {} {}'.format(s, s in keywords,\n",
    "                                                keywords_count[s] if s in keywords else 0 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# check if 'mot' is a key of 'key_count' with a test on the number of occurences   \n",
    "#----------------------------------------------------------------------------------\n",
    "def test_keyword(mot, key_count, threshold):\n",
    "    return (False , True)[key_count.get(mot, 0) >= threshold]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "narcism      -> narcissism   (init: [('narcissism', 1), ('narcism', 1)])\n",
      "apparition   -> shadow       (init: [('shadow', 3), ('phantom', 3), ('apparition', 1)])\n",
      "macao        -> macau        (init: [('macau', 1), ('macao', 1)])\n",
      "regent       -> trustee      (init: [('trustee', 1), ('regent', 1)])\n",
      "civilization -> culture      (init: [('culture', 2), ('civilization', 1)])\n",
      "ark          -> ark of the covenant (init: [('ark of the covenant', 2), ('ark', 1)])\n",
      "automaton    -> zombie       (init: [('zombie', 45), ('robot', 27), ('automaton', 1)])\n",
      "__________________________________________________________________________________________\n",
      "The replacement concerns 5.99% of the keywords.\n"
     ]
    }
   ],
   "source": [
    "keyword_occurences.sort(key = lambda x:x[1], reverse = False)\n",
    "key_count = dict()\n",
    "for s in keyword_occurences:\n",
    "    key_count[s[0]] = s[1]\n",
    "#__________________________________________________________________________\n",
    "# Creation of a dictionary to replace keywords by higher frequency keywords\n",
    "remplacement_mot = dict()\n",
    "icount = 0\n",
    "for index, [mot, nb_apparitions] in enumerate(keyword_occurences):\n",
    "    if nb_apparitions > 5: continue  # only the keywords that appear less than 5 times\n",
    "    lemma = get_synonymes(mot)\n",
    "    if len(lemma) == 0: continue     # case of the plurals\n",
    "    #_________________________________________________________________\n",
    "    liste_mots = [(s, key_count[s]) for s in lemma \n",
    "                  if test_keyword(s, key_count, key_count[mot])]\n",
    "    liste_mots.sort(key = lambda x:(x[1],x[0]), reverse = True)    \n",
    "    if len(liste_mots) <= 1: continue       # no replacement\n",
    "    if mot == liste_mots[0][0]: continue    # replacement by himself\n",
    "    icount += 1\n",
    "    if  icount < 8:\n",
    "        print('{:<12} -> {:<12} (init: {})'.format(mot, liste_mots[0][0], liste_mots))    \n",
    "    remplacement_mot[mot] = liste_mots[0][0]\n",
    "\n",
    "print(90*'_'+'\\n'+'The replacement concerns {}% of the keywords.'\n",
    "      .format(round(len(remplacement_mot)/len(keywords)*100,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KEYWORDS THAT APPEAR BOTH IN KEYS AND VALUES:\n",
      "---------------------------------------------\n",
      "shadow               -> dark                \n",
      "failure              -> loser               \n",
      "leech                -> parasite            \n",
      "carnival             -> circus              \n",
      "pit                  -> hell                \n",
      "drawing              -> lottery             \n",
      "deal                 -> mountain            \n",
      "twist                -> crook               \n",
      "pest                 -> plague              \n"
     ]
    }
   ],
   "source": [
    "# 2 successive replacements\n",
    "#---------------------------\n",
    "print('Keywords that appear both in keys and values:'.upper()+'\\n'+45*'-')\n",
    "icount = 0\n",
    "for s in remplacement_mot.values():\n",
    "    if s in remplacement_mot.keys():\n",
    "        icount += 1\n",
    "        if icount < 10: print('{:<20} -> {:<20}'.format(s, remplacement_mot[s]))\n",
    "\n",
    "for key, value in remplacement_mot.items():\n",
    "    if value in remplacement_mot.keys():\n",
    "        remplacement_mot[key] = remplacement_mot[value]             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb of keywords in variable 'plot_keywords': 8908\n"
     ]
    }
   ],
   "source": [
    "# replacement of keyword varieties by the main keyword\n",
    "#----------------------------------------------------------\n",
    "df_keywords_synonyms = \\\n",
    "            remplacement_df_keywords(df_keywords_cleaned, remplacement_mot, roots = False)   \n",
    "keywords, keywords_roots, keywords_select = \\\n",
    "            keywords_inventory(df_keywords_synonyms, colonne = 'plot_keywords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['woman director', 324],\n",
       " ['independent film', 318],\n",
       " ['duringcreditsstinger', 307],\n",
       " ['based on novel', 197],\n",
       " ['murder', 197]]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New count of keyword occurences\n",
    "#-------------------------------------\n",
    "keywords.remove('')\n",
    "new_keyword_occurences, keywords_count = count_word(df_keywords_synonyms,\n",
    "                                                    'plot_keywords',keywords)\n",
    "new_keyword_occurences[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# deletion of keywords with low frequencies\n",
    "#-------------------------------------------\n",
    "def remplacement_df_low_frequency_keywords(df, keyword_occurences):\n",
    "    df_new = df.copy(deep = True)\n",
    "    key_count = dict()\n",
    "    for s in keyword_occurences: \n",
    "        key_count[s[0]] = s[1]    \n",
    "    for index, row in df_new.iterrows():\n",
    "        chaine = row['plot_keywords']\n",
    "        if pd.isnull(chaine): continue\n",
    "        nouvelle_liste = []\n",
    "        for s in chaine.split('|'): \n",
    "            if key_count.get(s, 4) > 3: nouvelle_liste.append(s)\n",
    "        df_new.set_value(index, 'plot_keywords', '|'.join(nouvelle_liste))\n",
    "    return df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb of keywords in variable 'plot_keywords': 2121\n"
     ]
    }
   ],
   "source": [
    "# Creation of a dataframe where keywords of low frequencies are suppressed\n",
    "#-------------------------------------------------------------------------\n",
    "df_keywords_occurence = \\\n",
    "    remplacement_df_low_frequency_keywords(df_keywords_synonyms, new_keyword_occurences)\n",
    "keywords, keywords_roots, keywords_select = \\\n",
    "    keywords_inventory(df_keywords_occurence, colonne = 'plot_keywords')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['woman director', 324],\n",
       " ['independent film', 318],\n",
       " ['duringcreditsstinger', 307],\n",
       " ['based on novel', 197],\n",
       " ['murder', 197]]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New keywords count\n",
    "#-------------------\n",
    "keywords.remove('')\n",
    "new_keyword_occurences, keywords_count = count_word(df_keywords_occurence,\n",
    "                                                    'plot_keywords',keywords)\n",
    "new_keyword_occurences[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+b7FyThKniQ0iIiLinYDWiSuzNCZORERELpCSuACwwcEEc7YP9fjx0jXbSURE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\nF7I3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26043cb3160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph of keyword occurences\n",
    "#----------------------------\n",
    "font = {'family' : 'fantasy', 'weight' : 'normal', 'size'   : 15}\n",
    "mpl.rc('font', **font)\n",
    "\n",
    "keyword_occurences.sort(key = lambda x:x[1], reverse = True)\n",
    "\n",
    "y_axis = [i[1] for i in keyword_occurences]\n",
    "x_axis = [k for k,i in enumerate(keyword_occurences)]\n",
    "\n",
    "new_y_axis = [i[1] for i in new_keyword_occurences]\n",
    "new_x_axis = [k for k,i in enumerate(new_keyword_occurences)]\n",
    "\n",
    "f, ax = plt.subplots(figsize=(9, 5))\n",
    "ax.plot(x_axis, y_axis, 'r-', label='before cleaning')\n",
    "ax.plot(new_x_axis, new_y_axis, 'b-', label='after cleaning')\n",
    "\n",
    "# Now add the legend with some customizations.\n",
    "legend = ax.legend(loc='upper right', shadow=True)\n",
    "frame = legend.get_frame()\n",
    "frame.set_facecolor('0.90')\n",
    "for label in legend.get_texts():\n",
    "    label.set_fontsize('medium')\n",
    "            \n",
    "plt.ylim((0,25))\n",
    "plt.axhline(y=3.5, linewidth=2, color = 'k')\n",
    "plt.xlabel(\"keywords index\", family='fantasy', fontsize = 15)\n",
    "plt.ylabel(\"Nb. of occurences\", family='fantasy', fontsize = 15)\n",
    "#plt.suptitle(\"Nombre d'occurences des mots clés\", fontsize = 18, family='fantasy')\n",
    "plt.text(3500, 4.5, 'threshold for keyword delation', fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B418jUljEtHN2tGOofiiYPZmUHnvQ/NKbwRg2v/Ms6YOOo2T9agrnTCXUehci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cDd2f6gkYx99146du1L3/V2YfDWv+OFO36He5JNdz+d3LwCBm9zJO8/fRmfvfFvzGJss++lUZ9G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\nAED7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26043cbecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "#_____________________________\n",
    "# calculations of correlations\n",
    "corrmat = df_keywords_occurence.dropna(how='any').corr()\n",
    "#________________________________________\n",
    "k = 17 # number of variables for heatmap\n",
    "cols = corrmat.nlargest(k, 'num_voted_users')['num_voted_users'].index\n",
    "cm = np.corrcoef(df_keywords_occurence[cols].dropna(how='any').values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True,\n",
    "                 fmt='.2f', annot_kws={'size': 10}, linewidth = 0.1, cmap = 'coolwarm',\n",
    "                 yticklabels=cols.values, xticklabels=cols.values)\n",
    "f.text(0.5, 0.93, \"Correlation coefficients\", ha='center', fontsize = 18, family='fantasy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_var_cleaned = df_keywords_occurence.copy(deep = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_name</th>\n",
       "      <th>missing_count</th>\n",
       "      <th>filling_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>homepage</td>\n",
       "      <td>3091</td>\n",
       "      <td>35.644389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tagline</td>\n",
       "      <td>844</td>\n",
       "      <td>82.427649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>country</td>\n",
       "      <td>174</td>\n",
       "      <td>96.377264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>actor_3_name</td>\n",
       "      <td>93</td>\n",
       "      <td>98.063710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>language</td>\n",
       "      <td>86</td>\n",
       "      <td>98.209452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>actor_2_name</td>\n",
       "      <td>63</td>\n",
       "      <td>98.688320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>actor_1_name</td>\n",
       "      <td>53</td>\n",
       "      <td>98.896523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>director_name</td>\n",
       "      <td>30</td>\n",
       "      <td>99.375390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>overview</td>\n",
       "      <td>3</td>\n",
       "      <td>99.937539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>duration</td>\n",
       "      <td>2</td>\n",
       "      <td>99.958359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>title_year</td>\n",
       "      <td>1</td>\n",
       "      <td>99.979180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>release_date</td>\n",
       "      <td>1</td>\n",
       "      <td>99.979180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>num_voted_users</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>vote_average</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>movie_title</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>budget</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>spoken_languages</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>production_countries</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>production_companies</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>popularity</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>original_title</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>plot_keywords</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>id</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>genres</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>status</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>gross</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             column_name  missing_count  filling_factor\n",
       "0               homepage           3091       35.644389\n",
       "1                tagline            844       82.427649\n",
       "2                country            174       96.377264\n",
       "3           actor_3_name             93       98.063710\n",
       "4               language             86       98.209452\n",
       "5           actor_2_name             63       98.688320\n",
       "6           actor_1_name             53       98.896523\n",
       "7          director_name             30       99.375390\n",
       "8               overview              3       99.937539\n",
       "9               duration              2       99.958359\n",
       "10            title_year              1       99.979180\n",
       "11          release_date              1       99.979180\n",
       "12       num_voted_users              0      100.000000\n",
       "13          vote_average              0      100.000000\n",
       "14           movie_title              0      100.000000\n",
       "15                budget              0      100.000000\n",
       "16      spoken_languages              0      100.000000\n",
       "17  production_countries              0      100.000000\n",
       "18  production_companies              0      100.000000\n",
       "19            popularity              0      100.000000\n",
       "20        original_title              0      100.000000\n",
       "21         plot_keywords              0      100.000000\n",
       "22                    id              0      100.000000\n",
       "23                genres              0      100.000000\n",
       "24                status              0      100.000000\n",
       "25                 gross              0      100.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df = df_var_cleaned.isnull().sum(axis=0).reset_index()\n",
    "missing_df.columns = ['column_name', 'missing_count']\n",
    "missing_df['filling_factor'] = (df_var_cleaned.shape[0] \n",
    "                                - missing_df['missing_count']) / df_var_cleaned.shape[0] * 100\n",
    "missing_df = missing_df.sort_values('filling_factor').reset_index(drop = True)\n",
    "missing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RUVG44447IJd3KgwREREREXWB1VX3wYMHsXz5cuh0Ovj7+8NoNGL79u14//338eGHH6JP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\nEARBEAShgRDBuyAIgiAIgiA0EP8P3DAgoKF7cjkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x260411940b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_axis = missing_df['filling_factor'] \n",
    "x_label = missing_df['column_name']\n",
    "x_axis = missing_df.index\n",
    "\n",
    "fig = plt.figure(figsize=(11, 4))\n",
    "plt.xticks(rotation=80, fontsize = 14)\n",
    "plt.yticks(fontsize = 13)\n",
    "\n",
    "N_thresh = 5\n",
    "plt.axvline(x=N_thresh-0.5, linewidth=2, color = 'r')\n",
    "plt.text(N_thresh-4.8, 30, 'filling factor \\n < {}%'.format(round(y_axis[N_thresh],1)),\n",
    "         fontsize = 15, family = 'fantasy', bbox=dict(boxstyle=\"round\",\n",
    "                   ec=(1.0, 0.5, 0.5),\n",
    "                   fc=(0.8, 0.5, 0.5)))\n",
    "N_thresh = 17\n",
    "plt.axvline(x=N_thresh-0.5, linewidth=2, color = 'g')\n",
    "plt.text(N_thresh, 30, 'filling factor \\n = {}%'.format(round(y_axis[N_thresh],1)),\n",
    "         fontsize = 15, family = 'fantasy', bbox=dict(boxstyle=\"round\",\n",
    "                   ec=(1., 0.5, 0.5),\n",
    "                   fc=(0.5, 0.8, 0.5)))\n",
    "\n",
    "plt.xticks(x_axis, x_label,family='fantasy', fontsize = 14 )\n",
    "plt.ylabel('Filling factor (%)', family='fantasy', fontsize = 16)\n",
    "plt.bar(x_axis, y_axis);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>director_name</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4553</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     director_name actor_1_name actor_2_name actor_3_name\n",
       "4553           NaN          NaN          NaN          NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filling = df_var_cleaned.copy(deep=True)\n",
    "missing_year_info = df_filling[df_filling['title_year'].isnull()][[\n",
    "            'director_name','actor_1_name', 'actor_2_name', 'actor_3_name']]\n",
    "missing_year_info[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "budget                                                                  0\n",
       "genres                                                                   \n",
       "homepage                                                              NaN\n",
       "id                                                                 380097\n",
       "plot_keywords                                                            \n",
       "language                                                              NaN\n",
       "original_title                                 America Is Still the Place\n",
       "overview                1971 post civil rights San Francisco seemed li...\n",
       "popularity                                                              0\n",
       "production_companies                                                   []\n",
       "production_countries                                                   []\n",
       "release_date                                                          NaT\n",
       "gross                                                                   0\n",
       "duration                                                                0\n",
       "spoken_languages                                                       []\n",
       "status                                                           Released\n",
       "tagline                                                               NaN\n",
       "movie_title                                    America Is Still the Place\n",
       "vote_average                                                            0\n",
       "num_voted_users                                                         0\n",
       "title_year                                                            NaN\n",
       "country                                                               NaN\n",
       "director_name                                                         NaN\n",
       "actor_1_name                                                          NaN\n",
       "actor_2_name                                                          NaN\n",
       "actor_3_name                                                          NaN\n",
       "Name: 4553, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filling.iloc[4553]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_year(df):\n",
    "    col = ['director_name', 'actor_1_name', 'actor_2_name', 'actor_3_name']\n",
    "    usual_year = [0 for _ in range(4)]\n",
    "    var        = [0 for _ in range(4)]\n",
    "    #_____________________________________________________________\n",
    "    # I get the mean years of activity for the actors and director\n",
    "    for i in range(4):\n",
    "        usual_year[i] = df.groupby(col[i])['title_year'].mean()\n",
    "    #_____________________________________________\n",
    "    # I create a dictionnary collectinf this info\n",
    "    actor_year = dict()\n",
    "    for i in range(4):\n",
    "        for s in usual_year[i].index:\n",
    "            if s in actor_year.keys():\n",
    "                if pd.notnull(usual_year[i][s]) and pd.notnull(actor_year[s]):\n",
    "                    actor_year[s] = (actor_year[s] + usual_year[i][s])/2\n",
    "                elif pd.isnull(actor_year[s]):\n",
    "                    actor_year[s] = usual_year[i][s]\n",
    "            else:\n",
    "                actor_year[s] = usual_year[i][s]\n",
    "        \n",
    "    #______________________________________\n",
    "    # identification of missing title years\n",
    "    missing_year_info = df[df['title_year'].isnull()]\n",
    "    #___________________________\n",
    "    # filling of missing values\n",
    "    icount_replaced = 0\n",
    "    for index, row in missing_year_info.iterrows():\n",
    "        value = [ np.NaN for _ in range(4)]\n",
    "        icount = 0 ; sum_year = 0\n",
    "        for i in range(4):            \n",
    "            var[i] = df.loc[index][col[i]]\n",
    "            if pd.notnull(var[i]): value[i] = actor_year[var[i]]\n",
    "            if pd.notnull(value[i]): icount += 1 ; sum_year += actor_year[var[i]]\n",
    "        if icount != 0: sum_year = sum_year / icount \n",
    "\n",
    "        if int(sum_year) > 0:\n",
    "            icount_replaced += 1\n",
    "            df.set_value(index, 'title_year', int(sum_year))\n",
    "            if icount_replaced < 10: \n",
    "                print(\"{:<45} -> {:<20}\".format(df.loc[index]['movie_title'],int(sum_year)))\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fill_year(df_filling)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用名字中的信息来填充关键词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icount = 0\n",
    "for index, row in df_filling[df_filling['plot_keywords'].isnull()].iterrows():\n",
    "    icount += 1\n",
    "    liste_mot = row['movie_title'].strip().split()\n",
    "    new_keyword = []\n",
    "    for s in liste_mot:\n",
    "        lemma = get_synonymes(s)\n",
    "        for t in list(lemma):\n",
    "            if t in keywords: \n",
    "                new_keyword.append(t)                \n",
    "    if new_keyword and icount < 15: \n",
    "        print('{:<50} -> {:<30}'.format(row['movie_title'], str(new_keyword)))\n",
    "    if new_keyword:\n",
    "        df_filling.set_value(index, 'plot_keywords', '|'.join(new_keyword)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kLiqa/2c4Kp2WqAm278X9U3Ek7rKtl9394yLass45MWOGAVVuLbmjlsJFRYfF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\nhRBCCKuosBBCCGEVFRZCCCGs+h/8g89aR7tREwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26041239080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = corrmat.nlargest(9, 'num_voted_users')['num_voted_users'].index\n",
    "cm = np.corrcoef(df_keywords_occurence[cols].dropna(how='any').values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True,\n",
    "                 fmt='.2f', annot_kws={'size': 10}, \n",
    "                 yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用回归来进行填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZGfKQ4purUUAkFMBosYNhWGSly7HtygJsu7IgrBDqNAp8Y9ul40bP+DMOZtsX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26043a5f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.25)\n",
    "cols = ['gross', 'num_voted_users']\n",
    "sns.pairplot(df_filling.dropna(how='any')[cols],diag_kind='kde', size = 2.5)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def variable_linreg_imputation(df, col_to_predict, ref_col):\n",
    "    regr = linear_model.LinearRegression()\n",
    "    test = df[[col_to_predict,ref_col]].dropna(how='any', axis = 0)\n",
    "    X = np.array(test[ref_col])\n",
    "    Y = np.array(test[col_to_predict])\n",
    "    X = X.reshape(len(X),1)\n",
    "    Y = Y.reshape(len(Y),1)\n",
    "    regr.fit(X, Y)\n",
    "    \n",
    "    test = df[df[col_to_predict].isnull() & df[ref_col].notnull()]\n",
    "    for index, row in test.iterrows():\n",
    "        value = float(regr.predict(row[ref_col]))\n",
    "        df.set_value(index, col_to_predict, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "variable_linreg_imputation(df_filling, 'gross', 'num_voted_users')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_name</th>\n",
       "      <th>missing_count</th>\n",
       "      <th>filling_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>homepage</td>\n",
       "      <td>3091</td>\n",
       "      <td>35.644389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tagline</td>\n",
       "      <td>844</td>\n",
       "      <td>82.427649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>country</td>\n",
       "      <td>174</td>\n",
       "      <td>96.377264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>actor_3_name</td>\n",
       "      <td>93</td>\n",
       "      <td>98.063710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>language</td>\n",
       "      <td>86</td>\n",
       "      <td>98.209452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>actor_2_name</td>\n",
       "      <td>63</td>\n",
       "      <td>98.688320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>actor_1_name</td>\n",
       "      <td>53</td>\n",
       "      <td>98.896523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>director_name</td>\n",
       "      <td>30</td>\n",
       "      <td>99.375390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>overview</td>\n",
       "      <td>3</td>\n",
       "      <td>99.937539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>duration</td>\n",
       "      <td>2</td>\n",
       "      <td>99.958359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>title_year</td>\n",
       "      <td>1</td>\n",
       "      <td>99.979180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>release_date</td>\n",
       "      <td>1</td>\n",
       "      <td>99.979180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>num_voted_users</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>vote_average</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>movie_title</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>budget</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>spoken_languages</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>production_countries</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>production_companies</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>popularity</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>original_title</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>plot_keywords</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>id</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>genres</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>status</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>gross</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             column_name  missing_count  filling_factor\n",
       "0               homepage           3091       35.644389\n",
       "1                tagline            844       82.427649\n",
       "2                country            174       96.377264\n",
       "3           actor_3_name             93       98.063710\n",
       "4               language             86       98.209452\n",
       "5           actor_2_name             63       98.688320\n",
       "6           actor_1_name             53       98.896523\n",
       "7          director_name             30       99.375390\n",
       "8               overview              3       99.937539\n",
       "9               duration              2       99.958359\n",
       "10            title_year              1       99.979180\n",
       "11          release_date              1       99.979180\n",
       "12       num_voted_users              0      100.000000\n",
       "13          vote_average              0      100.000000\n",
       "14           movie_title              0      100.000000\n",
       "15                budget              0      100.000000\n",
       "16      spoken_languages              0      100.000000\n",
       "17  production_countries              0      100.000000\n",
       "18  production_companies              0      100.000000\n",
       "19            popularity              0      100.000000\n",
       "20        original_title              0      100.000000\n",
       "21         plot_keywords              0      100.000000\n",
       "22                    id              0      100.000000\n",
       "23                genres              0      100.000000\n",
       "24                status              0      100.000000\n",
       "25                 gross              0      100.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_filling.copy(deep = True)\n",
    "missing_df = df.isnull().sum(axis=0).reset_index()\n",
    "missing_df.columns = ['column_name', 'missing_count']\n",
    "missing_df['filling_factor'] = (df.shape[0] \n",
    "                                - missing_df['missing_count']) / df.shape[0] * 100\n",
    "missing_df = missing_df.sort_values('filling_factor').reset_index(drop = True)\n",
    "missing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df_filling.copy(deep=True)\n",
    "df.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "推荐引擎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gaussian_filter = lambda x,y,sigma: math.exp(-(x-y)**2/(2*sigma**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def entry_variables(df, id_entry): \n",
    "    col_labels = []    \n",
    "    if pd.notnull(df['director_name'].iloc[id_entry]):\n",
    "        for s in df['director_name'].iloc[id_entry].split('|'):\n",
    "            col_labels.append(s)\n",
    "            \n",
    "    for i in range(3):\n",
    "        column = 'actor_NUM_name'.replace('NUM', str(i+1))\n",
    "        if pd.notnull(df[column].iloc[id_entry]):\n",
    "            for s in df[column].iloc[id_entry].split('|'):\n",
    "                col_labels.append(s)\n",
    "                \n",
    "    if pd.notnull(df['plot_keywords'].iloc[id_entry]):\n",
    "        for s in df['plot_keywords'].iloc[id_entry].split('|'):\n",
    "            col_labels.append(s)\n",
    "    return col_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_variables(df, REF_VAR):    \n",
    "    for s in REF_VAR: df[s] = pd.Series([0 for _ in range(len(df))])\n",
    "    colonnes = ['genres', 'actor_1_name', 'actor_2_name',\n",
    "                'actor_3_name', 'director_name', 'plot_keywords']\n",
    "    for categorie in colonnes:\n",
    "        for index, row in df.iterrows():\n",
    "            if pd.isnull(row[categorie]): continue\n",
    "            for s in row[categorie].split('|'):\n",
    "                if s in REF_VAR: df.set_value(index, s, 1)            \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def recommand(df, id_entry):    \n",
    "    df_copy = df.copy(deep = True)    \n",
    "    liste_genres = set()\n",
    "    for s in df['genres'].str.split('|').values:\n",
    "        liste_genres = liste_genres.union(set(s))    \n",
    "    #_____________________________________________________\n",
    "    # Create additional variables to check the similarity\n",
    "    variables = entry_variables(df_copy, id_entry)\n",
    "    variables += list(liste_genres)\n",
    "    df_new = add_variables(df_copy, variables)\n",
    "    #____________________________________________________________________________________\n",
    "    # determination of the closest neighbors: the distance is calculated / new variables\n",
    "    X = df_new.as_matrix(variables)\n",
    "    nbrs = NearestNeighbors(n_neighbors=31, algorithm='auto', metric='euclidean').fit(X)\n",
    "\n",
    "    distances, indices = nbrs.kneighbors(X)    \n",
    "    xtest = df_new.iloc[id_entry].as_matrix(variables)\n",
    "    xtest = xtest.reshape(1, -1)\n",
    "\n",
    "    distances, indices = nbrs.kneighbors(xtest)\n",
    "\n",
    "    return indices[0][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_parameters(df, liste_films):     \n",
    "    parametres_films = ['_' for _ in range(31)]\n",
    "    i = 0\n",
    "    max_users = -1\n",
    "    for index in liste_films:\n",
    "        parametres_films[i] = list(df.iloc[index][['movie_title', 'title_year',\n",
    "                                        'imdb_score', 'num_user_for_reviews', \n",
    "                                        'num_voted_users']])\n",
    "        parametres_films[i].append(index)\n",
    "        max_users = max(max_users, parametres_films[i][4] )\n",
    "        i += 1\n",
    "        \n",
    "    title_main = parametres_films[0][0]\n",
    "    annee_ref  = parametres_films[0][1]\n",
    "    parametres_films.sort(key = lambda x:critere_selection(title_main, max_users,\n",
    "                                    annee_ref, x[0], x[1], x[2], x[4]), reverse = True)\n",
    "\n",
    "    return parametres_films "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sequel(titre_1, titre_2):    \n",
    "    if fuzz.ratio(titre_1, titre_2) > 50 or fuzz.token_set_ratio(titre_1, titre_2) > 50:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def critere_selection(title_main, max_users, annee_ref, titre, annee, imdb_score, votes):    \n",
    "    if pd.notnull(annee_ref):\n",
    "        facteur_1 = gaussian_filter(annee_ref, annee, 20)\n",
    "    else:\n",
    "        facteur_1 = 1        \n",
    "\n",
    "    sigma = max_users * 1.0\n",
    "\n",
    "    if pd.notnull(votes):\n",
    "        facteur_2 = gaussian_filter(votes, max_users, sigma)\n",
    "    else:\n",
    "        facteur_2 = 0\n",
    "        \n",
    "    if sequel(title_main, titre):\n",
    "        note = 0\n",
    "    else:\n",
    "        note = imdb_score**2 * facteur_1 * facteur_2\n",
    "    \n",
    "    return note"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_to_selection(film_selection, parametres_films):    \n",
    "    film_list = film_selection[:]\n",
    "    icount = len(film_list)    \n",
    "    for i in range(31):\n",
    "        already_in_list = False\n",
    "        for s in film_selection:\n",
    "            if s[0] == parametres_films[i][0]: already_in_list = True\n",
    "            if sequel(parametres_films[i][0], s[0]): already_in_list = True            \n",
    "        if already_in_list: continue\n",
    "        icount += 1\n",
    "        if icount <= 5:\n",
    "            film_list.append(parametres_films[i])\n",
    "    return film_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def remove_sequels(film_selection):    \n",
    "    removed_from_selection = []\n",
    "    for i, film_1 in enumerate(film_selection):\n",
    "        for j, film_2 in enumerate(film_selection):\n",
    "            if j <= i: continue \n",
    "            if sequel(film_1[0], film_2[0]): \n",
    "                last_film = film_2[0] if film_1[1] < film_2[1] else film_1[0]\n",
    "                removed_from_selection.append(last_film)\n",
    "\n",
    "    film_list = [film for film in film_selection if film[0] not in removed_from_selection]\n",
    "\n",
    "    return film_list   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_similarities(df, id_entry, del_sequels = True, verbose = False):    \n",
    "    if verbose: \n",
    "        print(90*'_' + '\\n' + \"QUERY: films similar to id={} -> '{}'\".format(id_entry,\n",
    "                                df.iloc[id_entry]['movie_title']))\n",
    "    #____________________________________\n",
    "    liste_films = recommand(df, id_entry)\n",
    "    #__________________________________\n",
    "    # Create a list of 31 films\n",
    "    parametres_films = extract_parameters(df, liste_films)\n",
    "    #_______________________________________\n",
    "    # Select 5 films from this list\n",
    "    film_selection = []\n",
    "    film_selection = add_to_selection(film_selection, parametres_films)\n",
    "    #__________________________________\n",
    "    # delation of the sequels\n",
    "    if del_sequels: film_selection = remove_sequels(film_selection)\n",
    "    #______________________________________________\n",
    "    # add new films to complete the list\n",
    "    film_selection = add_to_selection(film_selection, parametres_films)\n",
    "    #_____________________________________________\n",
    "    selection_titres = []\n",
    "    for i,s in enumerate(film_selection):\n",
    "        selection_titres.append([s[0].replace(u'\\xa0', u''), s[5]])\n",
    "        if verbose: print(\"nº{:<2}     -> {:<30}\".format(i+1, s[0]))\n",
    "\n",
    "    return selection_titres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________\n",
      "QUERY: films similar to id=12 -> 'Pirates of the Caribbean: Dead Man's Chest'\n",
      "nº1      -> Pirates of the Caribbean: Dead Man's Chest\n",
      "nº2      -> Pirates of the Caribbean: At World's End\n",
      "nº3      -> Pirates of the Caribbean: The Curse of the Black Pearl\n",
      "nº4      -> Pirates of the Caribbean: On Stranger Tides\n",
      "nº5      -> Cutthroat Island              \n"
     ]
    }
   ],
   "source": [
    "dum = find_similarities(df, 12, del_sequels = False, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________\n",
      "QUERY: films similar to id=12 -> 'Pirates of the Caribbean: Dead Man's Chest'\n",
      "nº1      -> Pirates of the Caribbean: The Curse of the Black Pearl\n",
      "nº2      -> Cutthroat Island              \n",
      "nº3      -> The Hobbit: An Unexpected Journey\n",
      "nº4      -> The 13th Warrior              \n",
      "nº5      -> Red Sonja                     \n"
     ]
    }
   ],
   "source": [
    "dum = find_similarities(df, 12, del_sequels = True, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________\n",
      "QUERY: films similar to id=2 -> 'Spectre'\n",
      "nº1      -> Spectre                       \n",
      "nº2      -> Skyfall                       \n",
      "nº3      -> Quantum of Solace             \n",
      "nº4      -> The Art of War                \n",
      "nº5      -> Kick-Ass 2                    \n"
     ]
    }
   ],
   "source": [
    "dum = find_similarities(df, 2, del_sequels = True, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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